GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
Authors: Heming Zhang
Di Huang
Wenyu Li
Michael A Province
Yixin Chen
Philip Payne
Fuhai Li
Venue: ICLR 2026 Poster (2026)
Keywords: Reinforcement Learning
Large Language Model (LLM)
Text-Numeric Graph (TNG)
Multi-Omics Integration
Explainability
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets, guiding the discovery of novel therapeutics and effective treatment strategies. Existing pipelines capture only one or two of these—numerical omics ignore topological context, text-centric LLMs lack quantitative grounded reasoning, and graph-only models underuse rich node semantics and the generalization power of LLMs—thereby limiting mechanistic interpretability. Although Process Reward Models (PRMs) aim to guide reasoning in LLMs, they remain limited by coarse step definitions, unreliable intermediate evaluation, and vulnerability to reward hacking with added computational cost. These gaps motivate jointly integrating quantitative multi-omic signals, topological structure with node annotations, and literature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context. To resolve this challenge, we propose GALAX (Graph Augmented LAnguage model with eXplainability), an innovative framework that integrates pretrained Graph Neural Networks (GNNs) into Large Language Models (LLMs) via reinforcement learning guided by a Graph Process Reward Model (GPRM), which generates disease-relevant subgraphs in a step-wise manner initiated by an LLM and iteratively evaluated by a pretrained GNN and schema-based rule check, enabling process-level supervision without explicit labels. As an application, we also introduced Target-QA, a benchmark combining CRISPR-identified targets, multi-omic profiles, and biomedical graph knowledge across diverse cancer cell lines, which enables GNN pretraining for supervising step-wise graph construction and supports long-context reasoning over text-numeric graphs (TNGs), providing a scalable and biologically grounded framework for explainable, reinforcement-guided subgraph reasoning toward reliable and interpretable target and pathway discovery in precision medicine.

Published as a conference paper at ICLR 2026
GALAX: GRAPH-AUGMENTED LANGUAGE MODEL FOR EXPLAINABLE REINFORCEMENT-GUDE SUB-
GRAPH REASONING IN PRECISION MEDICINE
Heming Zhang', Di Huang", Wenyu Lie, Michael Province Yixin Chen², Philip Payne', Fuhai Li 1D2BB Deett. CSE, 'Dept. of Genetics, Washington University St. Louis *Correspondence: fuhai. 1i@wustl.
ABSTRACT
In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signal- ing pathways and targets, guiding the discovery of novel therapeutics and effee- tive treatment strategies. Existing pipelines capture only one two of these, thereby limiting mechanistic interpretability. Although Process Reward Mod- els (PRMs) aim to guide reasoning in LLMs, they remain limited by coarse step definitions, unreliable intermediate evaluation, and vulnerability to reward hacking with added computational cost. These gaps motivate jointly integrating quantitative multi-omic signals, topological structure with node annotations, and siterature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context. To re- solve this challenge, we propose GALAX (Graph Augmented LAnguage model with e eXplainability), an innovative framework that integrates pretrained Graph Neural Networks (GNNs) into Large Language Models (LLMs) via reinforce- ment learning guided by Graph Process Reward Model (GPRM), which gener- ates disease-relevant subgraphs in a step-wise manner initiated by an LLM and iteratively evaluated by pretrained GNN and schema- based rule check, enabling process-level supervision without explicit labels. As an application, we also in- troduced Target-QA, benchmark combining CRISPR-identifeed targets, multi- omic profiles, and biomedical graph knowledge across diverse cancer cell lines, which enables GNN pretraining for supervising step-wise graph construction and supports long-context reasoning over text-numeric graphs (TNGs), providing a scalable and biologically grounded framework for explainable, reinforcement- guided subgraph reasoning toward reliable and interpretable target and pathway discovery precision medicine.
INTRODUCTION
Identifying therapeutic targets and elucidating discase mechanisms are main challenges in preci- sion medicine (Steyaert et al. 2023 Topol 2019). CRISPR- gene editing has revolutionized functional genomic by enabling high throuhhuu perturbation of gene function across diverse cel- lular contexts (Shalem et al. 2014 Li et al. 2023). In oncology, large-scale CRISPR screens in cancer cell lines and deriedt models have revealed context-specific genetic vulnerabilities, providing a robust experimental foundation for biomarker and target discovery (Shi et al., 2015). Despite these advances, computationally predicting key targets from multi-omic profiles and terpreting their mechanistic role in disease progression remains difficult. In particular, bridging omic data with interpretable explanations of molecular mechanisms continues to be critical un- met need (Zhang et al.) (2024a). Traditional approaches, such as differential expression analysis or essentiality scoring, lack the capacity to model the hierarchical and cross-modal dependencies in molecular networks, often overlooking key regulatory redundancies and pathway-level dynamics. Recent graph-basee models have shown promise in outcome prediction tasks (Ren et al. 2024), yet they typically lack the structured supervision necessary for accurate target prioritization and mech- anism discovery and seldom jointly integrate quantitative multi-omic features, topological structure with node annotations, and literature-scale text-limiting mechanistic interpretability.

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Meanwhile, Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. particularly through techniques like in-context learning (ICL) (Brown et al. 2020) and chain-of-thought (CoT) prompting (Wei et al. 2022), which enable multi-step reasoning. However, L.LMs often suffer from hallucination and lack grounding in struc- tured knowledge, especially in scientific domains. To mitigate these issues, Retrieval-Augmented Generation (RAG) (Lewis et al.) (020) and its graph-based variants, such as RoG (Luo et al. 2023), SubgraphRAG (Li et al. 2024), GNN-RAG (Mavromatis & Karypis 2025) and G-Retriever (He) et al. 2024). have been proposed to enhance LLM performance by incorporating external knowl- edge graph. Despite their utility, these approaches still focus on final answer accuracy and give little attention to the reliability of intermediate reasoning. The retrieved subgraphs are noisy, large, and lack ground-truth mechanistic structure, making supervised retrieval unstable. Most existing models also fail to integrate numerical omic signals, causing the loss of cell line-specific information needed for target discovery. On the other hand, the Process Reward Model (PRM) framework has been introduced to provide fine- aineggriieed supervision over intermediate steps in reasoning tasks (Luo
et al. 2024 Lightman et al., [003]] Uesato et al. 2022 Wang et al. 2023 PRMs provide step-wise supervision by assigning intermediate rewards reinforcement learning (RL) agents, forming the foundation for Large Reasoning Models (LRMs) trained with Reinforcement Learning with
Human Feedback (RLHF) (Bai al. 2022), Proximal Policy Optimization (PPO) (Schulman
et al. 2017). and Group Relative Policy Optimization (GRPO) (Shao et al. 2024). For example, StepGRPO (Zhag et al. (2025b) extends GRPO by incorporating rule-based step-wise rewards to supervise each intermediate reasoning step, addressing the sparse reward problem and enhancing multi-step reasoning in multimodal language models. However, PRMs face key limitations in defin- ing fine-grained reasoning steps, verifying intermediate correctness, and hacking for model-based rewards (Gao et al. 2023), further complicating training.
These challenges are amplified in biomedicine: reasoning over multi-omic, gene-regulatory text-numeric graphs (TNGs) lacks ground-truth stepwise annotations, making intermediate su- pervision infeasible, and the combinatorial explosion of biological paths renders exhaustive plan- ning or retrieval impractical. We propose GALAX (Graph-Augmented LAnguage model with eXplainability), which couples LLMs with pretrained GNN under reinforcement learning guided by a Graph Process Reward Model (GPRM). Instead of explicit labels, GALAX uses the GNN as stepwise supervisor and schema-basee rule term to check validity, scoring intermediate subgraphs (partial signaling cascades) for biological plausibility and cancer relevance to provide fine- graineddded, graph-based rewards. GALAX prompts an LLM to propose candidate targets from multi-omic pro- files and partial knowledge graphs, then an RL graph generator assembles task-specific cancer sub- networks under GPRM
language reasoning into interpretable graph construc- tion and yielding mechanistically grounded, patient-specific subnetworks for target prioritization. To evaluate, we introduce Target-QA, a benchmark integrating multi-omic data, biomedical graph knowledge, and CRISPR screening outcomes across diverse cancer cell lines. Together, GALAX and Target-QA deliver a scalable, reinforcement-guider solution for interpretable, patient-specific target identification and discase-mechannss discovery.
2 RELATED WORK
LLMs Augmented with Knowledge and Graph Structures Prompt tuning has emerged as a lightweight and scalable method for adapting LLMs to downstream tasks without full finetun- ing (Korbak et al., 2023 Lester et al. (02T). While effective, operates over flat text representations and struggles to incorporate structured domain knowledge or multi-modal signals. To address this, Retrieval-Augmenttd Generation (RAG) (Lewis et al.) 2020) and graph-augmented approaches such as G-Retriever (He et al.] 2024), RoG (Luo et al. 2023), SubgraphRAG (Li et al.) (2224) and GNN-RAG (Mavromatis & Karypis 2025) have been proposed. However, these methods depend on accurate subgraph retrieval and still lack support for reliable reasoning. Most existing models also fail to integrate numerical omic signals, causing the loss of cell linc-specific information needed for target discovery and leaving them poorly suited for large, patient-specifie text-numeric graphs such as multi-omic signaling networks.
Reinforcement Learning for Step-wise Reasoning Reinforcement learning has been instrumen- tal in aligning LLM behavior through methods like RLHF (Bai et al., (022). PPO (Schulman et al. 2017). and GRPO (Shao al. 2024). The Process Reward Model (PRM) (Luo et al.
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Figure 1: Representative paradigms of patient-specific target prediction in-context LLM workflows and comparison with our approach. (a) User-specific in-context prompts with basic query input. (b) Augmenta- tion with retrieved knowledge relevant to the query. (e) GALAX framework: combining user-specific prompts, biomedical knowledge, and reinforcement guided subgraph reasoning for explainable target prediction.
2024 enables step-wise supervision and has been adopted in Large Reasoning Models (LRMs). However, PRMs face key challenges: fine-grained step definitions are ambiguous, intermediate correctness is hard validate, and model-based rewards can lead to reward hacking (Gao et al. 2023). These challenges are particularly acute in biomedical domains, where reasoning is inher- ently unstructured and lacks step-wise annotations, making conventional PRM pipelines impractical.
Multi-omic Data Integration in Biomedical AI From a biological standpoint, the integration of genomic, transcriptomic, and proteomic has been essential for understanding disease mech- anisms and therapeutic vulnerabilities (Hasin et al.] 2017, Kristensen et al.] 2014). Traditional approaches rely on statistical fusion or dimensionality reduction (Meng et al., 2016;; [Shen et al., 2009 Rohart et al.) 2017: Argelaguet et al.) 2018 Nguyen & Wang 2020). which overlook the hierarchical and interconnected nature of molecular data. More recently. GNNNbaaed models like MOGONET (Wang et al.) 2021) and MoGCN (Li et al., 2022) have demonstrated the value of structured graph reasoning for cancer subtype classification and biomarker identification. However, these models are primarily designed for outcome prediction and often fall short in identifying actionable biomarkers associated with specific disease mechanisms.
GALAX addresses the limitations of existing models by introducing a RL-guided framework that dynamically constructs biologically relevant subgraphs for each patient or cell line. This enables in- terpretable, sentext-sensitive target prioritization that adapts to both multi-omic features and disease- specific graph as the text-numeric format. To our knowledge, GALAX is the first to unify numerical multi-omic signals, literature-scale textual information, and biological topology under a reinforce- ment learning paradigm with biologically grounded supervision, which learns to reason through step-wise subgraph generation, guided by an authoritative biomedical GRPM.
3 PROBLEM FORMULATION
Identifying key targets and uncovering disease mechanisms remains a major challenge in precision medicine. To address this, we adopt the Text-Omic Signaling Graph (TOSG) (Zhang et al. 2025a). which integrates multi-omic features and biomedical con- text into a unified graph structure. Built upon TOSG, our model GALAX couples LLM--based hypothesis genera- tion with reinforcement-guuse guided subgraph reasoning to en- able interpretable, patient-specific target predictions on the Target-QA benchmark.
Ialinna Micange Pispagation
PROTEIN
Fretce Nomee Betoeiiaain Froue
TOSG Construction We construct the TOSG by inte- grating three modalities: numerical omic evidence 2()) textual entity descriptions T, and topological information within unified biomedical knowledge graph G {V,&).
Emmgle bona Wo
Desccitioos
Globel Massage Propagation
Here, V represents the topological entities and È denotes the biological relations. The detailed formulation of each modality is presented as follows. Feature Space: Specifically, utilizing the DepMap as the source
Demonstration of TOSG Con- struction and Graph Foundation Model
Figure 2:
of numerical evidence (Dempseer et al. 2019). we formulate this raw data as ())
(x:(0
1==1>
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where X M comprises of OO samples with M entities. These samples are paired with binary labels for cancerous or non-cancerous cell lines, denoted as y(b) E {0, [0, 1, Textual Space: Each entity in the TOSG is associated with name and description, represented T (TammeTTess) where [Taaeel = [Tasse| = M. Topological Space: The M entities in each sample are composed of multiple components derived from genomic, transcriptomic, and proteomic contexts. These multi-omic features are integrated into the TOSG using an existing integration tool, BioMed- Graphica (Zhang et al., 2024b). resulting in a text-attributed knowledge graph 9 = (V. E). The set of vertices is defined as V (V(pm), V(s), p(t) V(r) ). representing promoter, gene, transcript, and protein entities, respectively. The size of each set is given by [V(em]] m!pm), ||<]] m9)
+ mm() M. Correspond- [D<0|] m²(1), and [V(p]] m(P), such that [V| = m (pm) mmf)) + m(()
ingly, we map the cell-specific omic features for the n--h sample, denoted as x=
E IRM using a
compact vector concatenation of the four entity types: x!!0
((mm) ① xx(1
@ x! @ x!!") where
xf? represents the feature vector for the respective modality (e.g., x!? E R corresponds to pro- tein levels). In detail, this graph can be decomposed into two subgraphs: G(mm) ( and g(PPT) = (>(PPn, C(PP))). Here, ((in) captures the internal signaling processes for protein translation. As shown in Figure[]] internal propagation follows the central dogma (Crick) 1970): promoter (pur- ple) - gene (red) + transcript (yellow) + protein (blue), with V = > and | = M. Mean- while, ((PP)) represents the gene regulatory network structured around protein-protein interactions (PPI), where y(PPI) V(r), In summary, the TOSG unifies numerical omic evidence, textual de- scriptions, and topological information into Text-Numeric Graph, defined G = (2((0) T, V.E)-
Target-QA Generation We construct the feature set X' by filtering for cancer cell lines that con- tain both comprehensive annotations and curated CRISPR-based target information, defining the collective multi-omic evidence as set X' {X,, N where X, E R. Subsequently, align this quantitative data with the reasoning capabilities of L.LMs, we systematically structure the input context for each instance by integrating textual metadata, molecular profiles, and interaction graphs, detailed as follows. Omies Information: To augment context with patient-specine data, we in- corporate the top-K features for the LLM. Due to input token constraints of LLMs (Achiam et al., 2023) and the presence of CpG sites with methylation beta values saturated at 1, which dominate rankings and render top-K selection uninformative, we derive a concise multi-omic representation x((K) = [gg (K) @ t @ p! ((k) by extracting the top K features from genomic, transcriptomic, and proteomic modalities. Here, g! (K). and pa k.k)) denote the ordered lists of selected gene, transcript, and protein names, respectively, which correspond directly to the omic-related graph nodes ()(omie) Sample Information: Disease entity has associated name and textual descrip- tion with S (Smame, Sdese where |S] m Leveraging annotations from DepMap about cell lines, we mapped the cell line and disease names in samples to form the sets C = {ca N the
and S' {s,, \ -= respectively. Disease-related Protein Subgraph: For cell line, C.. cell line related disease entity s', to retrieve from BioMedGraphica' disease-target interaction graph g(mm) = (DDD)) g g(VT)) where y(mI) = (D(()) y(P) includes disease and protein nodes. To pro- vide structured graph context, we apply a subgraph retrieval strategy that extracts disease-relevant protein entities 20(p)) C v(r) and their interactions c(DT) along with h-hop protein neighbors vi(A)
by extracting interactions from E(PP) where 2((h) C >V(p), This yields a sample-specifer subgraph gl = (V% E((ub), }. where V((uub) >((") U UV! with the full graph set G(suh) = (gg(" } I==1
Hence, each query Qn = fcn, S',, x((K) g((ub) is paired with answer An describing top-r CRISPR targets R., frn,1..... ), yielding instance Dn, = (Qn, A,) in dataset D {D, 1 Jum strati- fied by TCGA (Weinstein et al., 2013) types (e.g.. DI.AD, DARÇA).
Patient-Specific Target Prediction with Explainability GALAX is designed to generate not only accurate but also interpretable predictions by explicitly modeling the reasoning process through subgraph construction (see Figure 3). Given a query Qn, the model produces both a prioritized target list A, and an explanatory subgraph G,;:
Å,, G (Qn, T, E; 0L)
The function f is composed of three modules (see Section[4.2]: (1) an initial language model fin that performs coarse reasoning and extracts candidate entities via initial answering; (2) reinforcement-
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An overview the GALAX workflow. Given processed multi-omic profiles (genomic, transcrip- tomic, proteomic), a subgraph is retrieved by identifying disease assoccated proteins and their h-hop neighbors. Then (-n(() proposes initial targets, refined by reinforcement guided graph generator x x+)) supervised by a pretrained graph foundation model g(-). The final subgraph, G'. combined with the query, is passed to a second-stage LLM fima()) for target prediction. The full pipeline enables explainable, patient-specific reason- ing grounded in molecular biology and CRISPR evidence.
Figure 3:
based graph generator (-)) that incrementally constructs the explainable subgraph g under the guidance of a pretrained graph classifier g(-) parameterized by fG, using step-wise biological plau- sibility rewards; and (3) final language model fimal that refines the prediction by reasoning over both the initial output and the generated subgraph context. The subgraph 9, serves as a transparent rationale, offering deeper insights into the disease mechanism.
GALAX
4.1
FOUNDATION MODELS PRETRAINING
LLM Pretraining We pretrain a large language model, denoted as fLT with parameters 0TT using curated text corpora. The input data comprises omic entity descriptions T. disease annotations S. protein protein interactions g(PP), and bisease-targed relationships g(DT), As illustrated in Figure this pretraining phase equips the model with foundational knowledge of biomedical terminology and relational structure (see Appendix C.1). thereby enhancing its capacity for downstream reasoning in biomedical tasks. Then we will continue pretraining the language model finit parameterized by mntt
which are detailed in Appendix C.2
Graph Foundation Model Pretraining We pretrain the graph encoder 6p via two-stage pipeline (see FigureZ). In stage one, a unified graph- model fo is trained over node attributes (0),, textual features T, and edge set E, with mootiin-prttein interaction edges stochasti- cally masked as Emask ~ Bernoulli(p). p < 1. The resulting representation is
grw = f (x("), ,TE Emask)
(2)
where HPr E N xddi encodes contextualized entity states. In details, we generate edge mask Emask ~ Bernoullip)), where < is the ratio of the masked edges for (PPT) to mask out the signaling flows in protein- protein interactions. Then, we apply internal message propagation with
M in
GNN (ENC (X((), T), g(i))
(3)
where ENCpr is cross-modal encoder to align textual and omic features and H,me E cross No xM dn The first-stage pretraining captures gene regulatory patterns by performing masked global message passing over (PPI)) by
( (H (PPI)) Emack)
(4)
In the second stage, downstream model fo is initialized with pretrained parameters 0 and is used to predict disease types from multi-omic inputs. The predicted class for each sample is given by:
50(0)
arg max Softmax 2
MLPG (fo(x T, ,T,; (5)0))
(5)
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where O denotes the set of disease types, and fo consists of the same architecture as ENCcross GNN and GNNppI. The model is trained to minimize the cross-entropy loss between the predicted probability distribution by Softmax (MLPa(fa())) and the ground-trut truth label. The pretrained graph foundation model fa serves as a structural proxy to guide step-wise reasoning in downstream tasks.
4.2 MODEL TRAINING
The pipeline of model couples initial answering, reinforcemenn-guudder subgraph generator, and fi- nal answering. Given the structured prompt p,(iit) from query Q.. the pretrained model finit outputs ( and a biomedical named entity recognition (NER) 6 extracts entities Re(lnii and maps them to proteins y/! which will contribute to forming the start set (start)) depending on situation. Node features are embedded by pretrained graph foundation model to obtain Him and formed X, (((imd), Sub- graph construction is framed as reinforcement learning problem consisting of four elements: state 0 (V%= c(t)) ); action A (Nare v(g)) adds single edge under feasibility masks; policy
uses message propagation (MSG) module to produce x={²) TMG (g.%) X((and) and two masked probability function TSRC, FrGr sample vige and vigr: reward combines feedback from a pretrained classifier g(-), a rollout averaging L simulated continuations, and rule-based term Rule that penal- izes schema violations. We accept an action e only when (()) > 0 and update the generator with rewghd-weighted cross-entropy with early stopping; the best subgraph G] retained 52 stochastic run. For final answering, G% is verbalized in expert mode (Fatemi et al. 2023) and appended to
On to form p!(inaa); the model ffinal is finetuned with token-level cross-entropy against An. What follows is detailed exposition of the model design and training framework.
Initial Answering With the pretrained language model finit based on fL, parameterized by I the input to the model is a structured prompt p((ni) derived from the original query Qn. specificially for turning gl (((b)) into graph expert format (see Appendix C.2 for details), and it will output Afterwards, an NER function, Φ, is applied to extract biomedical entities from (imit) by
A (init)
(imit) = ((Aa))))) where R((mil) (In. (inil)
(init)
(imit) }. And which are then mapped to
corresponding protein nodes, (((iit) (u niinit, 1 (
nniiit,
Process Reward Graph Generator
The initial node set (stta))) is selected based on predefined priority: if the disease-related protein set ((()) available, the top rj most relevant entities are used; if not, the top 7 entities from the initialization set vifiiit) are selected. If both are unavailable, nodes randomly sampled from the omic-derived set (omii)) Formally,
Top-n(V (D)())
if 11(1 +0
(staa))
Top-n/( Top-n(V)(mm)) (imi))
else if (imi)) +
Sample-n( (omic) (mmomm)))
otherwise
The candidate set was generated based on Vi(cnd) (init)) U v((ab)) U ((mic)) and ( c v (((and))
And the features of X' are precomputed using graph encoder See with parameters 0g followed by pretrained modules ENCrros and GNN,, with parameters GG and gG respectively:
H/m GNN, (ENCoross (J (X, T, T, ( T: (( g(i)); B0) E E x
The candidate node features X(cand) are selected from the precomputed representation Hmn Then introduce reinforcement-guider graph generator, denoted as *T-), where the policy operates over sample-specific graph states and candidate sets, enabling personalized subgraph construction.
At step i, the current graph state is defined ((4) ( _.l after applying the action of constructing edge (vi w The next graph state gli+1) formed by adding an edge between a sampled source node o and target node v'g. To compute the probabilities of selecting these nodes,
we embed the node features from (( together with the candidate features (cand) as:
x (M))) X(cand)
(8)

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where TMsG consists of message propagation (MSG) module and feature selection mecha- nism that extracts node embeddings corresponding D from the propagated features. Based on embeddings, the source and target nodes are sampled according to the generated probabilities with
ge ~ T (x MMscc); Figt ~ TTGT (X MMror; see))
(9)
where TRRC and TroT are returned with probability implemented via MLPs and softmax by masking Msrc and MrGT, which restrict source selection to nodes in g and exclude the source when selecting the target. Based on the probability, nodes the and Fige will selected by the sampling
function. The selected node pair then forms the updated graph state g(i+1) for next step.
Reinforcement-Guuide Reward and Training To guide the graph generation process, we define a rollout-basee reward function that combines immediate classifier feedback with future trajectory simulation. At generation step i, the intermediate graph is represented as gli++1) (Vl++1) &n eltt1))
after applying the action of constructing edge (Vare "igi). .. We define g(-) as a pretrained graph classi- fier that computes class probabilities by applying GNN encoder GNN followed by projection head MLPg formally expressed as g(-) Softmax [MLP& (GNNPpr())) The model parameter- ized by pretrained weights Opp and M.Pp and outputs probability distribution over classes O. Let o" O denote the target class. The reward R,," for step is defined as:
R = 90 (g
1 0
+
Σ
go (Rollouts (g
(10)
|
Here, 9o (g) refers to the probability assigned to the target class 0', and Rollout,(.) simulates the (-th full trajectory by continuing generation from the current partial graph using the current policy. The hyperparameter À balances intermediate and future rollout-based feedback. To ensure reasoning
aligning with biological plausibility, we incorporate a rule-based reward term Rusee (g) ((++)) that penalizes invalid edges according to relations from BioMedGraphica. The final reward is:
R (i) total
R..0 + Arute Ruule (g(((1)
(11)
This formulation guides the generation process toward subgraphs that are both predictive of the tar- get class and consistent with domain specifcc biological priors. And we used the greedy acceptance where if R (i) i, the model will be trained with loss function,
total > 0, set (i+1)) as current state; otherwise keep the previous state. Then in each step
Catep
total [CE(e (X) .MSRc)+ + CE( (o TIGT (x(()) ,MrGr )
(1)
(12)
where the generator is optimized with rewirh-weighted cross-entropy (CE) function. In practice, we sample multiple candidate subgraphs under the policy parameterized by 0, across multiple runs Ω and select the optimal subgraph g
Final Answer Generation with Prompt Tuning The optimal subgraph G is converted into structured textual description via expert mode (see details in Appendix [.II.. which will be appended to original query Qn to form final graph-augmenter prompt P! (ffina)) With language model fimal based on pretrained finis, it generates the output sequence An {(nn, an. an,y') according to:
Ebrm (An Qn, Gi
II J=1
Esaaal (an.i) ān,<jj P p((ina))
(13)
To align the model output with the refined ground-truth answer An, which contains the top CRISPR- gene targets for sample n, we finetune the model by:
N J'
Cinal ΣV
log Eorn (an..j an,<jj < p(fina))
(14)
This objective encourages the model internalize the structured reasoning encoded in G to gener- ate biologically grounded answers. After generation, an NER function @ will extract entities from the model's output A, with R, = ((Å,) = (fn.1. f.. fn,s). These predicted protein targets used for evaluating biological relevance and overlap within reference targets in A,-
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5 EXPERIMENTS
Datasets We construct the Target-QA dataset over multiple TCGA cancer types using DepMap, in- tegrating omic features (epigenomic, genomic, transcriptomic, proteomic) and metadata from cancer cell lines. Each QA pair consists of an input query, including multi-omic and cell line infor- mation, and an output answer comprising the top-7 (7 100) CRISPR- targets. The final dataset contains 363 QA pairs from cancerous cell lines after integration and preprocessing. We use an 80/20 train-test split and repeat experiments across four randomized seeds to ensure stability and generalization. For foundation model pretraining, we collect dataset of 336 samples with multi-omic features from unannotated samples, including both disease and control groups (297 cancerous, 39 non-cancerous), and apply stratified sampling to address class imbalance. Full data processing and cohort composition are described in Appendix B
Experimental Setup We initialize the language model with LLaMA3-8B - (Grattafioni et al. 2024). pretrained on biomedical terminology and curated textual descriptions involv- ing
enhance domain- specific vocabulary and biological context understanding (see Figure 5). For graph encoding, we use BioBERT-vI.I (Lee et al.] 2020) for text embeddings and Graph Attention Networks (GAT) (Velickoić et al., 2017) to learn topological features from protein interaction graphs, incor- porating random edge masking to improve robustmess (see Figure 2 global message propagation). The pretrained GNN achieves 64.4% AUC in edge prediction, and 99.46% / 96.15% accuracy on disease type classification (train/test). Named entities are extracted using GPT-4o-mini via ChatGPT API (Hurst et al. GALAXX is trained using the Adam optimizer on two NVIDIA H100 GPUs (80GB). We set the number of top omic features per modality to ==10, the maximum subgraph rollout depth to L=5, and the number of candidate starting nodes 7 The reward formulation includes both rollout- and rule-based components, each weighted equally with x=1 and >ruk=1. The reasoning task is formulated protein neighborhood from the disease-annotated subgraph. Model outputs are evaluated using pre- cision, recall, Fl Fi-score, Jaccard similarity, H and Hit@ 10, by comparing predicted target sets
and disease- -isease-protein relationships from BioMedGraphica
binary classification problem (0)=2), using a 1-hop (h=1)
Rn against reference labels R., Additional details are provided in the Appendix D.2
Table 1: Performance of models across datasets and metries
Model GAT
Prodision 1
Reall!
Precison?
Recal 1
Posdiioo
Roall
20000
0.000
0.030es
0.033
G0XX5s ave
LS-TTMae Uniex
LISTTMMsdi Unics RG
1.3.TION
Onies
0.615563.00
0002
0.4900s 0:9165n000
G-Rutrora pre GAT
0
0,202 Loaai
B.4642 aass
0 m9
0.414
0.5722 w0a0
0.5380e1
0.191
CNN-RAGG
0. 11733loone
8.500ie eas
0.5352e1 am
D 1200 csu
GALAX (Qw125578))
0,644513m4
0.5470 0.54051n80m
0.5462.0m11
Table 2: Hit@ 10 and Hit@5 for models across datasets
Usema
LUAD
BBRA
Madel
10e
MET
040900
(11833)
(LSERR)
GIAE
L3 Onscs KG
011:22
C000011
(LI333
M-FTTMolii Onicc
A0000L0000
(L000) 442.0
Ouscc
0.013111.0
0.0003 L000
0 LIMGG
1.FFTIQA-
0.58010000
L.S-TIQAA)- -niic- KG
0
0857993 m0
0.78555 823
B.57780 C103
pre
ReG
0,8356a3
B.767 3417
GIN--LG
GALAX GALAX 5-78)
0.851511.0
0.92209 2844
B 0.880llo BMD
11 0189001004
B. 1L888le
08841_400s
110077000011
8.5647 J4
0,00000 LAPPO
Baseline Models Traditional method, M2T (Multiomie2Targee(Dee)) et al.) 2024)), serves as a baseline that uses only multi-omic features without graph or language modeling and performs poorly across all metrics. And we perform ablation studies to isolate the contribution of core compo- nents of language and graph modules in GALAX. On the language axis, a non toonttsk-tunnd LLaMA3 (L3+0mics) is weak; domain-adaptin finetuning on biomedical text (L3-FT(Med)+Omiice yields modest gains; task-adaptive finetuning on Target-QA (L3-FT(QA))Omicss produces the step- change. On the graph axis, GAT incorporates graph foundation model and trained to predict the
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(+)
Overall Average
(b)
Figure 4: Model performance and analysis. (a) Overall performance across metries. (b) LUAD (ACH- 000860): cancer-relevant subgraph highlighting disease-associated nodes (purple) and srriihmenttsuppoorte edges (orange). (c) Enrichment analysis: top pathway and discase terms with p-values and gene counts.
CRISPR knockout effects, shows limited improvements. Furthermore, integrating graph modules into language models comes with different outputs. Adding static KG to each language foun- dation models ([L3/ / L3-FT((Med) / L3-FT(QA))+0mmss+K model performances, and graph retrieval with pretrained GAT (G-Retriever+pre-GATT outper- form some task-adaptive finetuned language models but not reliably due to the difficulty of ex- tracting relevant subgraphs from millions of nodes/edges. RoG, SubgraphRAG (Li et al.) 2024), and GNN-RAG (Mavromatis & Karypis (025) augment the language model by retrieving optimal paths, achieving moderate improvements over L3-Fs(QA))Omii<KKG Reinforcement-guiide sub- graph construction top of QA-tuned language (GALAX:
barely improve or even decrease
(o00s))mmicsKG--HPPH consistent, cross-datasct gains of roughly 2%-5% on each metric. indicating that a reinforcement- guided subgraph generator outperforms other graph augmented models across all datasets by en- abling process-level reasoning over biologically plausible subgraphs (shown in Tables 2 Details of baseline models are provided in Appendix D.3
delivers
Computational Complexity We denote the language model complexity by Okk). The retrieved KG sub- graph includes M nodes, and denotes the graph em- bedding cost. When augmented with KG retrieval, L3- Omiess) introduces an additional ((M)) per query, yielding +(x+ + M²) for both training and inference. G-retriever+preGAT embeds all M nodes and thus incurs O(Me + e) RoG and GNN-RAG follow the same cost at retrieval, since they embed entities and relations, requir- ing O(Me + M²s). SubgraphRAG reduces this by retrieving only relation triplets, which costs O(M²e) at retrieval. And G-retreiver. RoG, GNN-RAG and SubgraphRAG all requires an O(s) at both training and inference for language models. GALAX augments the language model with reinforcement-guicce guidedd subgraph construction, with embedding cost O(ME + Mf.). The model re- quires ((x) forward pass initialize the top 1) candidates and another for final answer generation;
Model
Training & Inference
+O-iss AAFT(QAAOOmess+A SubgraphRAG
O(s)
O(k M²)
O(k + Mf2))
G-rerrieverrpreGGT
O Me M2e)
RoG
O(k + Ms MPe)
GNN-RAG
O(x Mr + MPs)
GALAX
O(x + Me + M²e)
Table 3: Complexity comparisons
since 1 < M, the graph-embedding term dominates the RL reward cost. Overall, the training and inference complexity of GALAX is therefore O( + Me + M2&) (see Table 3).
Main Results Tables 2 summarize the performance of GALAX and several competitive base- lines on the full test dataset. GALAX outperforms all baselines on every metric, reaching an overall precision of 0.5472 and recall of 0.5332. To further assess target prioritization quality, we report Hit@ 10 and Hir@5 in Table where GALAX again achieves the highest accuracy with an overall Hit@ 10 of 0.8815 and @it@5 5 of 0.9249. We also replaced the backbone with Qwen2.5-7B-Innste Instruct and observed similar performances on all metrics, indicating that GALAX maintains stable perfor- mance under backbone changes (See Appendix [D.4 for full experiment results). We further tested generalization by forming three holdout sets in which the selected TCGA cancer types were excluded from training and treated as unseen during evaluation. Across all holdout sets, GALAX showed only modest performance declines, indicating that it preserves strong accuracy and generalizes reliably to previously unseen cancer types (Details are provided in Appendix [.5). Beyond Target-QA, we evaluate GALAX on the pediatric cancer dataset from PedDep (Dharia et al. 2021). which offers multi-omic profiles and CRISPR- targets for 31 tumor cell lines, using zero-shot inference due to its small sample size. Even under this setting, GALAX surpasses all baselines on all metrics,

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Table 4: Overall performances for ablation studies omic inputs and KG structure
Coudig GALAX
Setting
Recall
Precisin
E20
Drop 22% edges
0.5061 coucce
0.5362 uvare
0.874L a000
B40
Fing 19% rage
0.817T ams
Drop edgas 80%
0188077 J00003
BB0
0.4563.00017
ND0
Dre 10% mites
0.503 mis
8.875an ano
0:851 wno
Ftog 4555 motes
2501
BeTTkan wes
0.850G was
Drop modes
0.2229
Omii-M M
Remose opigetomicc data
0.51 62 mes
0 018572000145
Omi-G
Remssve actertic data
0.2:21 ausez
0.4277 anaes
B 85550a0a0r
B1841Q auaas
Omi-TT
Remose zasactiphomi:
(18672 20u007
Onic All
0.3750.0a0
0,225
0.7967
GALAX
Origiea
L.FTTIQA) Quil
R30
Dro 10%
11521
0 88857
B40
Dmmp 47% edges
0.000
0 28 1115
0.83322
B69
Drop 03% edges
22570000022
0.4050 uoaee
B 8.220 40u20
08811 203175
E30
Drop 90% edgas
0.551 Loceds
018060
ND0
Drog 30% modes
4536
018229
N89
Dee 33% modes
0.2558
0.4558
0 0,8720
0,8350 ananss
N80
Drvp 037% modes
(12548
0.47 424722200 axass
8 2857 Mt.see
0.7600
N890
Frog 80% mosee
0 7111
01697400 2111
Ontic
Romsve opigeeouni
4.757
0.8786
01800620011
Omni-
Bomese cata
0.52 mau
0.5062
0 0.845
0.830
Onni-T
Remsse Trassrigoenic data
11 0-250620 2ee
0 0.5500 ain
0.8151 on 200007
Omni-P
Remove proceomic data
1:300000000
0-2872 0003
0 08007 2045
Omic-All
Remove oniics
0.7577 awaes
0.7558 05500
Onigrod
0.
0 0.879
0.802
showing strong transfer to external dataset (Details are provided in Appendix [.6].. Figure 4 illustrates the explainable subgraph generated for the lung cancer cell line ACH-000860 To further validate the biological relevance of the extracted subgraph, we performed functional enrichment analysis. The results reveal significant enrichment in cancer-associated signaling pathways, includ- ing the cancer pathway WP5434 and EGFR-related receptor signaling pathways such as WP138 and WP3680, as cataloged in WikiPathways (Agrawal al. (024)). Notably, EGFR a well-established therapeutic target in NSCLC (Steuer & Ramalingam] 2015) appears in five enriched terms, together with PTK2 and 6 which are known regulate invasion, epithelial mesenchymal transition, and therapeutic resistance (Tong et al. 2019; Sun et al. 2012). The observed pathway enrichment provides strong biological support for the relevance of the selected targets in lung cancer. Additional disease enrichment using the GAD DISEASE database further supports this conclusion (Sherman al. 2022), , with lung cancer identified as the top associated disease term with a p value of 0.0022, involving GSTM3, APAFI, NOD2, MLLT3, GC and EGFR. Full enrichment details across all can- cer types are included in the Appendix E. In addition, human and LLM evaluation results in
Appendix E.2 show that most generated subgraphs are biologically plausible.
Ablation Studies As shown in Table 4 we evaluated GALAX under systematic perturbations to omic inputs and KG structure. The KG provides both the guidance for graph generation and the re- trieved subgraphs that supply biological context to the language model, so removing portions of the KG naturally reduces overall performance. Under KG deletions, GALAX remained stronger than the baseline across all ddge-removal levels because node attributes and pretrained GNN embeddings enabled the model to assemble meaningful subnetworks from sparse structure. Node deletion was more destructive since removing 20% of nodes removed about 35% of edges, but GALAX main- tained more stable @it@5 and Hit @ 10 scores than the baseline across all deletion levels. Removing epigenomic data produced the smallest decline due to methylation saturation, while removing ge- nomic, transcriptomic, or proteomic signals caused larger drops, yet GALAX still outperformed the L3-FT(QA))Omie+KKG baseline even when all omics were removed. Overall, these findings show that GALAX generalizes well under shifts in omic distributions and reduced KG connectivity.
6 CONCLUSION
We present GALAX, a graph- graph-augmented language model that unifies numerical multi-omic ev- idence, literature-scale textual information, and stepwise graph construction under reinforcement learning with biologically grounded supervision through a Graph Process Reward Model (GRPM), which scores intermediate subgraphs for biological plausibility and cancer relevance and guides the system to generate patient-specific mechanistic subgraph for target prioritization without explicit labels. To facilitate evaluation, we introduce Target-QA, benchmark combining multi-omic data, CRISPR outcomes, and graph knowledge for target discovery. GALAX consistently outperforms baseline models this dataset.
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ETHICS STATEMENT
This dataset is derived from the Broad Institute's 's Cancer Dependency Map (DepMap) and will be released strictly for non- non-commerciaa, internal research and academic use, consistent with DepMap's Terms of Use. We not redistribute original DepMap files; instead, we provide derived, non- identifiable annotations and processing scripts/pointers rectly from DepMap after accepting its terms. The dataset not intended for clinical applications and must not be used for any Commercial Use (e.g., direct sale, incorporation into product, or Prainingddeelloinn/eennaaae
users can obtain the source data di-
ML/AI models beyond internal academic research). Users agree to acknowledge DepMap and the Broad Institute using the acknowledgement wording specified by DepMap, and to respect any third-party rights that may attach to the underlying data. Users must preserve confidentiality and refrain from any re-identification attempts. This statement summarizes our compliance posture and does not constitute legal advice; users are responsible for ensuring their own compliance with DepMap's Terms and applicable policies.
REPRODUCIBILITY STATEMENT
We release the source code together with preprocessing pipelines, thereby enabling reproduction of Target- QA datasets and reported experiments. The Target- Q. and GALAX are publicly available Huggingface and GitHub.
ACKNOWLEDGMENTS
This research was partially supported by NLM IR0ILM013900-01AL
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv:2303.08777 2023.
Ayushi Agrawal, Hasan Balcı, Kristina Hanspers, Susan L Coort, Marvin Martens, Denise N Slenter, Friederike Ehrhart, Daniela Digles, Andra Waagmeester, Isabel Wassink, al. Wikipathways 2024: next generation pathway database. Nucleic acids research, 52(DI):D679- DD89, 2024.
Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, and Oliver Stegle. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Molecular systems biology. [4(6)x81144 2018.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 2022.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 13:1 2020.
Francis Crick. Central dogma of molecular biology. Nature, 227(5258):561-56;) 1970.
Joshua M Dempster, Jordan Rossen, Mariya Kazachkova, Joshua Pan, Guillaume Kugener, David Root, and Aviad Tsherniak. Extracting biological insights from the project achilles genome-scale crispr screens in cancer cell lines. BioRxiv, pp. 720243, 2019.
Eden Z Deng, Giacomo B Marino, Daniel JB Clarke, Ido Diamant, Adam C Resnick, Weiping Ma, Pei Wang, and Avi Ma' ayan. Multiomies2targets identifies targets from cancer cohorts profiled with transcriptomics, proteomies, and phosphoproteomics. Cell Reports Methods, 4(8), 2024.
https: http//aaaapppgg/p///// /tttass//demmaporr/pptttaa

---

Published as a conference paper at ICLR 2026
Neekesh V Dharia, Guillaume Kugener, Lillian M Guenther, Clare F Malone, Adam D Durbin, Andrew L Hong. Thomas P Howard, Pratiti Bandopadhayay. Caroline Wechsler, Iris Fung, et al. A first-generation pediatric cancer dependency map. Nature genetics, 53(4))529-538, 2021.
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. Talk like a graph: Encoding graphs for large language models. arXiv preprint arXiv:2310.04560, 2023.
Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In International Conference on Machine Learning, pp. 10835-10866. PMLR, 2023.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The Ilama 3 herd of models. arXiv preprint arXiv:2407.21755 2024.
Yehudit Hasin, Marcus Seldin, and Aldons Lusis. Multi-omics approaches to disease. Genome biology, 18(1):33, 2017.
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. G-retriever: seriival--aumenterr generation for textual graph understanding and question answering. Advances in Nearal Information Processing Systems, 37: 37:132876-132907, 2024.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024.
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel Bowman, and Ethan Perez. Pretraining language models with human prefer- ences. In International Conference on Machine Learning, pp. 17506-17533. PMLR, 2023.
Vessela N Kristensen, Ole Christian Lingjærde, Hege G Russnes, Hans Kristian M Vollan, Arnoldo Frigessi, and Anne-Lise Børresen-Dall Principles and methods of integrative genomic analyses in cancer. Nature Reviews Cancer, 14(5):299-313, 2014.
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jae- woo Kang. Biobert: pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, (664):1234-1244 2020.
Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv: 104.08691, 2021.
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval- augmented gener- ation for mnowledge-intensiie nip tasks. Advances in neural information processing systems, 33: 9459-9474, 2020.
Mufei Li, Siqi Miao, and Pan Li. Simple is effective: The roles of graphs and large language mod- attrival-auggmntte generation. arXiv preprint arXiv:2410.20724
els in www.wldge-graph-baeee 2024.
Tianxiang Li, Yanyan Yang, Hongzhao Qi, Weigang Cui, Lin Zhang, Xiuxiu Fu, Xiangqin He, Meixin Liu, Pei-feng Li, and Tao Yu. Crispr/cas9 therapeuties: progress and prospects. Signal transduction and targeted therapy, 8(1):36, 2023.
Xiao Li, Jie Ma, Ling Leng, Mingfei Han, Mansheng Li, Fuchu He, and Yunping Zhu. Mogen: multi-omics integration method based on graph convolutional network for cancer subtype analy- sis. Frontiers in Genetics, 13:806842, 2022.
Hunter Lightman, Vincet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, llya Sutskever, and Karl Cobbe. Let's verify step by step. In The Twelfth International Conference Learning Representations, 2023.
12

---

Published as a conference paper at ICLR 2026
Liangchen Luo, Yinxiao Liu, Rosanne Liu, Samrat Phatale, Meiqi Guo, Harsh Lara, Yunxuan Li, Lei Shu, Yun Zhu, Lei Meng, et al. Improve mathematical reasoning in language models by automated process supervision. arXiv preprint arXiv:2406.06553 2024.
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprini arXiv:2310.01061, 2023.
Costas Mavromatis and George Karypis. Gnn-rag:
Graph neural retrieval for efficient large lan- guage model reasoning on knowledge graphs. In Findings of the Association for Computational Linguistics: ACL 2025, pp. 16682-16699, 2025.
Chen Meng. Oana A Zeleznik, Gerhard G Thallinger. Bernhard Kuster, Amin M Gholami, and Aedín C Culhane. Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in bioinformatics, 17(4):628 641, 2016.
Nam D Nguyen and Daifeng Wang. Multiview learning for understanding functional multiomics. PLoS computational biology, 16(4)ee0006677 2020.
Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, and Chao Huang. survey of large language models for graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discov- ery and Data Mining, pp. 6616-6626, 6626, 2024.
Florian Rohart, Benoît Gautier, Amrit Singh, and Kim-Anh Lê Cao. mixomics: An r package for "omics feature selection and multiple data integration. PLoS computational biology, 13(11): e1005752, 2017.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06344 2017.
Ophir Shalem. Neville Sanjana, Ella Hartenian, Xi Shi, David A Scott, Tarjei Mikkelsen, Dirk Heckl, Benjamin L Ebert, David E Root, John G Doench, et al. Genome-scale crispr-cas9 knock- out screening in human cells. Science, 343(6166):84 87,44877711 2014.
Zhihong Shao, Peiyi Wang. Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi. Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
Ronglai Shen, Adam B Olshen, and Mare Ladanyi. Integrative clustering of multiple genomic data types using joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics, 25(22):2906-9111 2009.
Brad Sherman, Ming Hao, Ju Qiu, Xiaoli Jiao, Michael W Baseler, H Clifford Lane, Tomozumi Imamichi, and Weizhong Chang. David: web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic acids research, 50(W W2266:221- 2022.
Junwei Shi, Eric Wang, Joseph P Milazzo, Zihua Wang, Justin Kinney, and Christopher R Vakoc. Discovery of cancer drug targets by crispr-cas9 screening of protein domains. Nature biotechnol- ogy, (3(6))661-66) 2015.
Conor Steuer and Suresh S Ramalingam. Targeting egfr in lung cancer: Lessons learned and future perspectives. Molecular aspects of medicine, 45:67-73, 2015.
Sandra Steyaert, Marija Pizurica, Divya Nagaraj, Priya Khandelwal, Tina Hernandez-Boussard, An- drew Gentles, and Olivier Gevaent. Multimodal data fusion for cancer biomarker discovery with deep learning. Nature machine intelligence, 5(4):351-362. 2023.
Yu Sun, Judith Campisi, Celestia Higano, Tomasz M Beer, Peggy Porter, Ilsa Coleman, Lawrence True, and Peter S Nelson. Treatment-induces damage to the tumor microenvironment promotes prostate cancer therapy resistance through wnt16b. Nature medicine, 1359-1133311388 2012.
Xuexia Tong, Ryosuke Tanino, Rong Sun, Yukari Tsubata, Tamio Okimoto, Mayumi Takechi, and Takeshi Isobe. Protein tyrosine kinase 2: a novel therapeutic target to overcome acquired egfr-tki resistance in non-small cell lung cancer. Respiratory Research, 20(1):270, 2019.
13

---

Published as a conference paper at ICLR 2026
Eric Topol. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1):44-555 2019.
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprini arXiv:2211.142775 2022.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv: 2017.
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ec-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thoure thouttt reasoning by large language models. arXiv preprint arXiv:2305. 04091, 2023.
Tongxin Wang. Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding, and Kun Huang. Mogonet integrates multi-omics data using graph convolutional networks allowing patient classi- fication and biomarker identification. Nature communicationss, 12(1):3445, 2021.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-2488 242327, 2022.
John N Weinstein, Erie A Collisson, Gordon B Mills, Kenna Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, and Joshua M Stuart. The cancer genome atlas pan-cancer analysis project. Nature genefics, 45(10): 111-:112111111111 2013.
Heming Zhang, Yixin Chen, Philip Payne, and Fuhai Li. Using deepsignalingflow mine signaling flows interpreting mechanism of synergy of cocktails. npj Systems Biology and Applications, 10 (1):92, 2024a.
Heming Zhang, Shunning Liang, Tim Xu, Wenyu Li, Huang, Yuhan Dong, Guangfu Li, J Philip Miller, S Peter Goedegebuure, Marco Sardiello, al. Biomedgraphica: An all-in-one platform for biomedical prior knowledge and omic signaling graph generation. bioRxiv, 2024b.
Heming Zhang, Tim Xu, Dekang Cao, Shunning Liang Lars Schimmelpfennig. Levi Kaster, Di Huang, Carlos Cruchaga, Guangfu Li, Michael Province, et al. Omnicelltosg: The first cell text-omic signaling graphs dataset for joint Ilm and gnn modeling. arXiv preprint arXiv:2504.00 02148, 2025a.
Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vI: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arxiv:2503.129377 2025b.
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A THE USE OF LARGE LANGUAGE MODELS
We used ChatGPT-5 edited by the authors, who take full responsibility for the final content. The LLM is not author and is not eligible for authorship under the ICLR Code of Ethics.
writing assistant. All Lug-sggeetted text was reviewed, fact-checked, and
B DATASET
B.1 BIOMEDICAL TERMINOLGY CORPUS
Gane Description
Disease Description
DUXB short for double hameobox is protein coding gane located Chromosame 16 from 75800893 75701461. detais, double homaceoo B the NCBI Gene descristion with Predicted anable DNA-ainding transcritionn polymerase poymersse I1-specfic transotption regulatory region sequence speaifi regulation of transcritiion by RNA polymerase Predicted to be activ in nucieus Asice from that. DUXB related to the tollwing genes: CPHXL2 CPHXL. TRIM33B FAM MBDSLS, DPRX PRAMEF25 SMCHD1 MBCOLS, ARGFX UNCX, DUKA, LRRC37A2. LRRC3TA, DREX, NOTO, LEUTX, KOMEE, ANHX, SDHD. CUM2, TIMM23, SHOX, GSC2, CRK TINMA44 TIMMIA LRRC37A3, NOBOX,
Lung adenocartinoms is diseass, which has been recorded MONDO with MONDO Name lung adenocarcinoma and MONDO is also mocrded UMLS with UMLS C0152013. detaiis adanccardnnama
activity.
MONDO description lung presence of malgnant glanduaar optholial maln prodiaccioon with
carcnoms
hinding activlly Predicled be
tamala nalo Usually lung adenocaronoms is asymptoretic and dentilled through screening stucies or as an incidental radilogio inding. ciinccal symptoms are present, they intude shorhess of bmatth eough. nemophysa, chest pain. and fever. Tatescso smake is known risk factor. Mutaiions KRAS, EGFR, BRAF, and ERBBZ genes are associated with this canoer Aside from that related following genes: TLRs, IPOS
adenccarinome
fPre
Figure Biomedical Corpus Composition. Pretraining LLM f( on biomedical terminology and struc- tural patterns using protein-proter and discase-protein interactions.
Each entities nodes in TOSG has the name and description about it with T (Trame, Taese } where [ name Teesel = M. And disease entity has has an associated name and textual descrip- tion with S = (Smmme Saesc] } where |S] (S), In the Figure 5 we pretrained _ with curated text copora BioMedGraphica where they provided the data collection and integration source code. Following their processed descriptions, by appending protein-protenn (PPI) and disease-protein (DTI) interaction information as textual de- assign scriptions after each protein and disease entity. For entities without known interactions, empty strings during corpus construction, resulting in the intermediate representations T and S. In practice, we exclude these empty entities to derive the final input sets T' and S', where T' includes 42,224 protein descriptions and S' includes 22,340 disease descriptions (i.e., IT'l = 42,224 and |S'| M²(S) = 22,340). This yields combined corpus of 64,564 text samples for pretraining.
incorporate G(PPD and (DTT)) to enrich the corpus
B.2 DEPMAP DATA PREPROCESSING
As shown in Figure op. after multi-omies integration in BioMedGraphica of DepMap cohort (see Table 6) comprises (U) (0) 985) cell-line samples, which are organized into three datasets: pretraining set, Target-QA, and Drug-QA. Of these 985 samples, 336 samples lack disease/tcga- code annotations or belong to non-cancerous samples, while 649 samples cancerous. Within the annotated cancer set, 363 samples overlap with DepMap CRISPR multi-omic data. Since we would like to utilize as many as samples for training GALAX, we set N = 363 as Target-QA, (0) 336
http:///gggiiiaaeecooooaaas///// L.11 lab aMedGraphica
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[0]
(c)
2M
(M4
Target-QA Samples Distribution: Train
206
300
Mor-rremete
[bj
TCBA Coueo
Figure 6: Dataset composition, feature landscape, and split statistics. (a) Block diagram of the corpus partitioned by phenotype (non-cancerous vs. cancerous) and by task: pretraining pool (pink), Target-QA (bluc), and Drug- QA (yellow). Numbers within boxes denote sample counts; the dashed horizontal line marks the train/test division and the dashed vertical line marks non-cancerousscancerus boundaries. (b) Heatmap of standardized multi-omic features (top 5,000 most variance features) for Target-QA samples. Rows are samples (annotated by TCGA cancer type); columns are features with top variances. The colored sidebar encodes TCGA cancer types (legend at right). (c) Distribution of Farget-QA samples by TCGA code for train (blue) and test (red); totals shown the insct (N 363; train 300 (82.6%), 63 (17.4%)).
and annotattdd/annootteedd
Table 5: TCGA cancer type codes and their full names.
TOGA Code
Full Name
TCGA Code
Full Name
Overall Average
Overall Average
Medulloblastoma
LUAD
Lung Adenocavinoma
ALL
Acute Lymphoblastic Leukemia
BRCA
Breast Invasive Carcinoma
LGG
Brain Lower Grade Glioma
COAD/READ
Colon/Rectun Adenocarcinoma
NB
Neuroblastoma
PAAD
Pancreatic Adenocarcinoms
MESO
Mesotheliona
GBM
Gliohlastoma Multiforee
LIHC
Liver Hepatocellular Carcinoma
SARC
Sarcoma
LAML
Acute Mycloid Leukemia
OV
Ovarion Scrous Cystadnnocarciooma
DLBC
Lymphoid Nooplasm Diffuse Large B-cell Lymphoma
SKCM
Skin Cutancous Melanoma
MM
Multiple Myeloma
ESCA
Esophageal Carcinoma
KIRC
Kidney Renal Clear Coll Carcinoma
SCLC
Small Cell Lung Cancer
THCA
Thyroid Carcinoma
HNSC
and Neck Squamous Carcinoma
BLCA
Bladker Urothelial Carcinoma
LUSC
Lung Squamous Cell Carcinoma
UCEC
Uterine Corpus Endometrial Carcinoma
STAD
Stomach Adenocarcinoma
PRAD
Prostate Adenocancinama
as pretraining set. To pretrain the graph foundation model, f fa. we consider |O] = classes cancerous (297 samples) and non-cancerous (39 samples) and perform 80/20 random split, yielding 269 training samples (238 cancerous, 31 non-cancerous) and 67 test samples (59 cancer- ous, 8 non-cancerous). The raw omics feature matrices include promoter and gene modalities m (pm) and m() (each with 86,238 entities), transcript modality (t) (412,039 2,39 transcript-level entities), and a protein modality m²) (121,419 protein-level entities). The promoter modality represented as virtual node type in the graph encoder entity-wise duplicates of genes whose omic values are drawn from DepMap methylation. Together, these modalities yield M = 834,809 omics entities (see Table 7). The unified knowledge graph integrates protein-protein interactions (|ε) = 17.087,977
and disease-target associations (IE(PTI) = 17,151,453) Every node in the Text-Omic Signaling Graph (TOSG) carries text attributes T = [Tname. Tass)) with |m = [aase = M; empty fields set to the empty string to preserve schema alignment. Hence, construct the TOSG by link- ing multi-omics features with biomedical relational knowledge using BioMedGraphica and pretrain the graph encoder fore ff (parameters opre ,0c) with a masked-edge modeling objective that en- courages recovery of held-out interactions from context, capturing implicit omic relationships and signaling dependencies.
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Table 6: Descriptions and sources of raw data files used in this study
File Type
File Name
Download Site
Promoter feature
CCLE RRBS. TSS1kb 20181022.xxt
https://deppap.orgg
portal/ data hatpa/aataapaaaaaaaaeeaaae 2tae
aliData
Gene feature
OmicsCNGene.csv
https:/ hetps://depmpp.ooo wwtp/pppppppooooo/
portal/ /attall/dttaaao hatpaaaaaaaaaaaaaa/aaaea/e wwwppaaaeeaaaa atab
a allbata
Transcript feature
See footnote
allData
Protein feature
protein quant current normalized.csv
https: haaps://deppin /dppapppppppppp///// org/
portal/date /dttal page/ atab
allData
CRISPR gene effect
CRISPRGeneEffeett.ss
https:/
httes//depmpppo..
portal/cata_page 7tab
allData
Cell line annotation
Table_S Ttble.S..Smmll...ffmmtttoo..
portal/data ?tab allbata
Cell line annotation
cellosaurus.obo
h5tps:/ http://pppppp......./ .rgg//y.rg//
batabeses/eelooaan
cel 1osaurus.coo
Cell line status
cell-inesii--NnnCCaeerouusss
https: Sstps://depmat
org otg/portal/context/
Non-Cancerous
Transcript name: ott.://eweeee.t..e..eoeooooooooo
Table 7: Summary of feature dimensions across omics and samples
Modality
Raw Matrix
Processed Matrix
Promoter
21,337 rows, 846 samples
86,238 entities, 985 samples
Gene
38,590 rows, 1,928 samples
86,238 entities, 985 samples
Transcript
19,138 rows, 1,672 samples
412,039 entities, 985 samples
Protein
12,755 rows, 378 samples
121,419 entities, 985 samples
Cell Line Annotation
1,019 samples
985 samples
Non-cancer Samples
137 samples
39 samples
B.3 TARGET-QA GENERATION
Based on the raw data provided from the DepMap CRISPR gene effect data with 1178 samples, we get the overlapped 363 (N = 363) samples with the pretraining samples. And we do 80/20 train/test. split with 80/20 ratiofor 300 training samples and 63 test samples at random seeds. In total, we collected test samples from LUAD (7 samples), BRCA (6 samples), COAD/READ (5 samples),
PAAD (4 samples), ESCA (3 samples), GBM (3 samples), OV (3 samples), SARC (3 samples), SCLC (3 samples), SKCM (3 samples), HNSC (2 samples), LUSC (2 samples), STAD (2 samples),
etc. (see Figure (C). Given that methylation values in Dep.ap have so many are 1 (full methylated the promoter region around the trasnscription start site), which means that many values ranked as top K (K=10), so we just omit the methylation (epigeonomic) values by only providing geomic, transcriptomic and proteomic values. Afterwards, each QA sample is indexed by a unique key corresponding to the cancer cell line, such as:
ACH-000098: The identifier for glioblastoma cancer cell line.
The corresponding JSON object contains the following fields:
cell_ineennme Name of the cancer cell line (c.g., GAMG).
sample dti index: Index for omies numpy data be fetched.
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disease:
Name of the associated disease (e.g., glioblastoma).
disease_bmgc.ite BMGC_DS00965),
BioMedGraphica-Com identifier of the disease (e.g.,
input:
top k gene, top transeript, top k.protein:
*
s: List of gene/transcrippprotein names.
* protein.bmgc.idee Corresponding BioMedGraphica- identifiers.
* protein Imnmmme ame _s: Other synonymy names or IDs for corresponding genesttranseripts/proeiii knowledge
di sease_protee Includes bmgc_ ds, da, hgnc_symbol s, and indi ces for discasc-associated proteins.
* ppi.neighbors: PPI-linked proteins with similar structure as above.
* protein.relationsiil Textual descriptions of biological interactions
(e.g. "BRCA1 TP53").
ground
Contains the validated target(() used for evaluation:
hgnc_ symbo HGCN symbol names for CRISPR targets
protein stireiinmmge__ds.. Correpsonded CRISPR targets names for BioMedGraphica- Conn names
-protein_] Imname_ i Other synonymy names IDs
This hierarchical structure supports multi-modal reasoning by organizing omic features, biomedical knowledge graphs, and ground-truth target labels, where the cell line name is denoted as Cu. the disease name as 8n, the top-ranked genes, transcripts, and proteins as x-K) and the associated knowledge graph (sub)
C PRETRAINING OF FOUNDATION MODELS
C.I LLM PRETRAINING ON BIOMEDICAL CORPUS
We pretrain large language model, denoted as Ste with parameters 00t using curated text corpora from final imput sets T' and S'. Hence, by pretraining the Llama3-8B-Instuue for epochs using per-device batch size of 16 and gradient accumulation step of 8, resulting in effective batch size of 128. The optimizer was AdamW with a learning rate of 1e-5, no weight decay, and a cosine learning rate schedule with warm-up ratio of 10%. Gradient clipping a maximum gradient norm of 1.0. To improve memory efficiency during training, we enabled gradient checkpointing and utilized bf16 precision while disabling fpl Training proceeded for 336 steps, with the loss decreasing from approximately 2.2 at the start to around 0.7 by the end (see Figure
applied with
Baw Across Fue
Smoothed
9u12
Dan 11 63227)
3uy4
6.1657
222
(Bell
28
Parh (Rial E17M))
1.25
18-
38-
Tran E130
Te Saeig.te S100
Figure 7: Pretraining language model loss biomedical corpus
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Initial Answering Raw Loss (Every Steps)
1.6
Run
Run
1.4
0.8
0.6
0.2
50
100
150
200
250
300
350
Training Step
Figure 8: Initial langugage answering training loss
C.2 LLM PRETRAINING ON TARGET-QA QA
We will supervise the language model using the refined answer A, the ground-truth label from Target-QA QA with:
N
Cinit
log Ep (and an, p(init))
(15)
where pfinit) is the structured prompt as shown in Table 8 This objective enables the model to refine its initial answering based on user-specific and naive graph information. In details, for each sample On. a question constructed Qn {cn, S/2 x!K) _.!bb)), ). which integrates the cell line identifier, disease label, multi-omics features, and a knowledge subgraph specific to the sample context. The corresponding answer A, is sentence that enumerates the top y DRISPR-Prioritizer gene targets for sample n, denoted as R, = {rn,1.
Jn,y). Each data instance is represented
as a tuple D, (Qn; An), and the full dataset given by D (D1. D2,..., Dx). }. The input to the model is a structured prompt p(init) derived from Qn. in which the knowledge subgraph g((ub) is translated into natural language format designed for expert-level graph reasoning. For example, a subgraph g% (sub) may contain nodes BRCAI, TP53, EGFR, MAPK1, AKTI, PIK3CA, TP53, TP53 - EGFR, EGFR + MAPK1, EGFR -> AKTI, AKTI -> MTOR, PIK3CA + AKTI, PIK3CA - PTEN, PTEN + MTOR, and MAPKI + CDK2. This subgraph defines the molecular context for reasoning about gene knockout effects in the cell line en under disease condition s',,. We conduct five independent finetuning trials using the constructed Target-QA dataset, each initialized with different random seed. The resulting training loss trajectories are shown in Figure 8 Among these, the best-performing run (run 5) exhibits stable convergence, beginning with an initial loss of approximately 1.3 and reaching a final loss of around 0.3. We select this run the final model checkpoint and designate as our initial model fint. which serves as the foundation for downstream reasoning and refined target prioritization.
MTOR, PTEN, and CDK2, with observed interactions: BRCA1
For pretraining finit with parameterized Omnt, we pretrain for 5 epochs with per-device batch size and gradient accumulation 2 (effective global batch size 2 GPUs), using AdamW (adamw_tore) orrch) with learning rate 1 x 10-5 cosine schedule with 10% warmup, and gradient clip- ping at 0.5. We enable gradient checkpointing and bfl 6 (with fp disabled), run 2x H100 80GB with DeepSpeed ZeRO-3. Checkpointing and evaluation occur every 67 steps with up to checkpoints retained. Data loading uses 4 workers, pinned memory, last-batch drop, and no length grouping; we set seed= 42.
C.3 GRAPH FOUNDATION MODELS
Pretraining for Capturing the Edge Mechanism We pretrain a graph model, denoted as f with parameters ege using pretraining samples from (0) To effectively model graph-structured biological relationships, pretrain our model using masked edge prediction objective com-
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Table Prompt design for Pilii and expected oulput for initial protein target reasoning.
Section
Content
Instruction
Identify the 100 priority genes whose knockout causes the strongest negative effect on the viability or proliferation of cell line co in the context of disease 8,, based on multi-omics and knowledge graph signals.
Input
Top 10 ranked genes with amplification from copy numher data: ((n)
(()) ((n)
.91 9'..,99
TTop 10 ranked transcripts with high expression: e ((m)
(n)) ((m)
....." "
Top 10 ranked proteins from RPPA: ((() PP2 PK
Diseaas--ssoociateee associated proteins from the knowledge graph: ((
Known protein- protein/diieass--roeee relationships: )(!
Output
Based the integrated multi-omic data and knowledge graph, identified the 100 genes whose knockout is predicted to have the most severe negative impact on the viability or proliferation of the on cell line in s/,. The prioritized gene list is as follows:
1. (imil)
2. F..2
3. T n,3
(inn)
...
These genes represent critical vulnerabilities for the given cell line under the disease context.
bined with random walk-based graph sampling. The process starts by masking a small fraction (p = 0.0001) of edges, allowing the model to infer missing interactions based on surrounding omics and text-derived features. We adopt a GAT-based encoder with two layers, each consisting of 8 hidden channels. Decoder layers use 4 channels. Both encoder and decoder modules apply dropout a rate of 0.2. The model supports optional batch normalization and uses a leaky relu u activation function. An internal encoder stack of up to 4 layers enables deeper relational mod- cling. All architecture and training options are managed through a reproducible argparse interface. Multimodal inputs include one omic feature and a text embedding of dimension 1, initialized using BioBERT v1.1. The model optionally supports training of the text encoder via train.text. We pretraining batch size of 4 for omies data and 64 for text. The optimizer is Adam W with a learning rate of 0.001, weight decay of 5 5x and gradient norm clipping at 1.0.
The pretrained model is evaluated using average loss, AUC, and average precision (AP) over valida- tion batches. As shown in Figure 9 the model demonstrates progressive improvement across steps, reaching minimum batch loss of 0. 140, peak AUC of 0.644, and peak AP of 0.619. These results confirm that the model successfully learns meaningful edge semantics and multimodal associations during pretraining. All training is conducted on a single NVIDIA H100 GPU (80GB).
Epoclstavel Nutrics During Pretrainigg
0.6
AvG inst
AUC
03
0.6
0.5
0.4
0.3
Emochh
Figure 9: Pretraining loss to capture gene regulatory mechanism
Pretraining for Capturing the Cancerous Status Due to the severe class imbalance in the train- ing set, we apply random oversampling the minority class (non-cancerous) to balance the class
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Table 9: Refined prompt design for Pallalt and output with graph context
Section
Content
Instruction
Based on initial LLM reasoning and subsignaling gene regulatory net- work identified by the subgraph generator, please identify the 100 priority genes whose knockout causes the strongest negative effect on the viability proliferation of cell line en the context of disease s,
Input
TTop 10 ranked genes with amplification from copy number data:
((n) '....99'
(())
TTop 10 ranked transcripts with high expression: (7(m)
(m)
Top 10 ranked proteins from RPPA: pi ,...., PK
((n)
Disease-associated proteins from the biomedical knowledge graph: V!
Known protein-protein/diseenn prottiin relationships: E(!ub)
Identified Subsignaling Gene Regulatory Network from Graph Generator
Involved genes in best connected subgraph: (<amm)
Inferred signaling cascade (edge text): g(
Refined Reasoning
Based the integrated multi-omies data and knowledge graph, identified the 100 genes whose knockout is predicted have the most severe negative impact on the viability or proliferation of the c, cell line in sy The priori- tized gene list is as follows:
fn.1
f,.2
3, fn.3
These genes represent critical vulnerabilities for the given cell line under the disease context.
distribution during the pretraining of the graph encoder fa- We pretrained a Graph Attention Net- work (GAT) model See with parameters 0et and selected the best-performing checkpoint based on test accuracy. The selected model achieved a training loss of 0.036, training accuracy of 99. 99.46%, and training Fl score of 0.996. On the test set, it obtained loss of 0.370, accuracy of 15%, and Fl score of 0.973, demonstrating strong generalization to both classes.
D EXPERIMENT DETAILS
D.1 PROMPT AND CONTEXT DESIGN FOR FINETUNING
Same as aforementioned prompt design shown in Table we construct the final-stage prompt pitinal), which incorporates both the initial answering and the subgraph-based regulatory context, as detailed in Table [ We perform five additional finetuning trials using this refined prompt format. The corresponding training are illustrated in Figure I0] where the best run (run 5) demonstrates smooth convergence and selected as the final model checkpoint frnal for generating the ultimate target predictions.
D.2 HYPERPARAMETERS
For finetuning, the model is trained for 5 epochs with a per-device batch size of 1 and a gradient accumulation step of 2, resulting in effective batch size of 2. We use the AdamW optimizer (adamw_torch) with a learning rate of x 10-5, no weight decay, and cosine learning rate scheduling with 10% warm-up ratio. Gradient clipping applied with a maximum gradient norm of 0.5. To support memory efficiency and large model training, we enable gradient checkpointing and use bf1 6 precision (with fp1 6 explicitly disabled). Training accelerated using DeepSpeed Stage 3 parallelism, configured via an external JSON file. All experiments are conducted using 2 NVIDIA H100 GPUs, each with 80GB of memory. The DeepSpeed configuration enables ZeRO Stage with both optimizer and parameter offloading to CPU, memory pinning, communication overlap, and gradient contiguity. Key parameters include automatic tuning of reduce bucket sizes and micro-bate batch size, uub-grouping disabled, and support for 16-bit weight gathering upon model saving. Gradient accumulation steps, clipping. learning rate, weight decay, and total training steps all set " "auto" for adaptive scaling. Warmup is controlled via Wa rmupDeaay,.R, which
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adjusts the schedule based on total steps. Model checkpoints and evaluation are performed every 67 steps, with up to 5 checkpoints retained. Logging is performed at every step. For data loading, use 4 dataloader workers and retain all batches (i.c., drop last=False). Both the initial answering finetuning and second stage finetuning for fimal-
To generate high-quality and biologically reasonable subgraph explanations, we implement ceffooremeet-guuddee generator, denoted as xT.), which operates over sample-specific graph states and candidate sets. To enhance robustness under noisy or unstable generation dynamics, intro- duce a retry-based mechanism that adaptively tunes key hyperparameters with multiple runs (V/ = 6, and da denotes the current number of runs), dynamically adjusting the configuration encourage ex- ploration and improve convergence. Specifically, the number of training epochs is reduced from 5 to a minimum of 2. promoting faster reinitialization. Simultaneously, the learning rate is increased linearly from an initial value of 1 x 10-3 to 0.001 . (1 + +)) to escape local minima. The maximum number of nodes per graph is reduced from 200 to 100 in steps of 25. The maximum number of graph construction steps i per rollout is similarly reduced from 50 to 20 in steps of 5. The retry mechanism enables efficient navigation of the search space while preserving biological plausibility through domain-specifte priors embedded in the reward formulation. To further study the effect of sparse versus dense rewards, we vary the reward calculation frequency by introducing a step interval parameter Specifically, when = 2. we compute the reward only every 2 steps during graph generation. Larger values of s produce sparser, more delayed reward signals, while smaller values (approaching 1) provide denser, more immediate feedback. As shown in Table TO GALAX demonstrates consistent stability across all reward calculation intervals. Notably, performance im- proves as rewards become denser (smaller s), with optimal results achieved at s 1, the default GALAX configuration. This trend indicates that the graph generator benefits most from immediate biological feedback provided at each step by the graph process reward model. In contrast, less fre- quent reward calculations (larger s) introduce noisier, quality. Importantly, even under highly sparse reward conditions (e.g., s 10), GALAX consis- tently outperforms the baseline L3-FT(QA)) + Omics + KG, demonstrating robustness to reward sparsity.
estimates that dilute the signal
Table 10: Ablation study on reward density controlled by step interval s
Reward Interval (s)
Precision
Recall f
F1 Score
Jaceard
Hites
Hit@ f
10
0,5312 :: 0.0044
0,5163 1 0.0019
0.5232 = 0.0031
0.3582 0.0035
0.8996
0.0050
0.8753
0,00H
0.5347 0.0062
0.5224 0.0043
0.5284
0.0054
0.3619 0.0055
0.9063
0.0079
0.8771
0.539% + 0.0051
0,5218 + 0.0036
0.5316
+ 0.0044
0,3657 + 0.0044
0.9024
-D.0799
0.8794
0.040
0,5389 : 0.0054
0,5278 : 0.0025
0.5302
0.0041
0.3673
1 0.0046
0.9137
-000556
0.8789
0.0038
0.0057
0.5296
0.0032
0,5384
0.0040
0.9043
0.9218
0.5806
0.0033
(GALAX)
0.5472
0.0033
0,5332
+ 0.0031
0.5399
0.0041
0.3726
0.0037
0.9249
0.0048
0.8815 0.0033
L.3FT(QA)) + Omics KG 0.5185 + 0.0240
0,4908 + 0.0402
0.5038
0.0327
0,3393 0.0298 0.8794
0.0114 0,8529 0,0133
Furthermore, GALAX employs four RL components to collectively prevent reward hacking. First, the frozen graph oncogenicity classifier (Graph) derives reward signals from a pre-trained, frozen foundation model rather than a co-trained reward model, thereby eliminating co-evolution exploits. Second, the schema-based rule term (Rules) validates each action against biomedical knowledge graph ground truth to ensure that proposed edges conform to established biological constraints. Third, the rollout-based future reward (Rollout) evaluates each action based its long-term consequences, preventing the generator from exploiting local reward irregularities or pursuing my- opic gains. Fourth, stepwise quality gating (Gating) rejects actions yielding negative cumulative reward, ensuring that only biologically valid steps-those satisfying both schema rules and onco- genicity criteria-are retained. Together, these four components constitute a multi-faceted defense against reward hacking. To validate this design, we conduct an ablation study by systematically removing each component. As shown in Table [I] the robustness of GALAX does not stem from any single component but rather from the interplay of all four mechanisms. The rollout and gating modules help avoid sight-sighted decisions and filter out invalid reasoning paths, while the graph supervisor and schema rules provide the underlying constraints that encourage the model to gener- ate biologically plausible subgraphs. Notably, removing either the graph supervisor or the schema rules leads to consistent performance drops, suggesting that the frozen graph oncogenicity classifier offers reliable biological guidance, and that the schema constraints effectively block structurally in- correct actions. In sum, these mechanisms work collectively to push the generator toward producing biologically plausible subgraphs rather than outputs that score well but lack plausibility, making our RI. considerably more robust than prior approaches.
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Table 11: Ablation study of reward components
Model Variant
Procision
Recall f
F1 f
Jaccard f
Hite5
Hite 10 f
(3.FTIQA) Omics
0.5250 Lovonse
0.5094 0.4555
0.3449 10.a23
L3-FTTQA) Omics + KG
0.5185eaoo24
0.4908e0.00
0. 6.303880.0027
0.877N620.0114
+.8529
GALAX w/o Graph
0.53 0455
0.527
0.5336 0013
0.3680
a.0048
0.
GALAX w/o Rules
0.5395
0.5221 Lu.oute
0.3570 L0.007s
0.8974.10.07
1.874610..042
GALAX w/o Rollout
0.5362499.000
0.5196e0.0022
0.32271 50.0046
0.3628800.008
0.9067 +0,0002
GALAX w/o Gating
0.5387=2.0050
0.52(3=0.0042
0. 0.5308 20004
0.3651 =0 o008
0.9118 8800.006
0.8782+0.0046
GALAX (Full Model)
0.5472 00.0053
0.5332
0.5399
0.3726:0.umr
0. 0.9249.u.0oe
0.8815
Final Answering Raw Loss (Every 10 Steps)
0.7
Run
0.6
Run
Run
0.5
0.4
0.3
0.1
0.0
50
100
150
200
250
300
350
training Step
Figure 10: Final langugage answering training loss
D.3 BASELINE MODELS
Multiomic2Target Multiomics2Targe is a statistical and knowledge-based framework that inte- grates transcriptomics, proteomics, and phosphoproteomics to identify cancer-specific therapeutic targets. It leverages curated background knowledge databases and enrichment algorithms to com- pare tumor profiles against normal tissues, aiming highlight targets uniquely activated in cancer. Specifically, the method filters out candidates that lack protein-level evidence or are not phosphory- lated, and prioritizes those enriched in oncogenic pathways and subtype-specific signaling patterns. The workflow accounts for tumor heterogeneity by enabling both subtype-level and patient-specific analyses, thereby offering a safer and more context-aware target identification strategy. However, as a non-learning-baie based baseline, Multiomies2Targee relies purely on statistical associations, which limits its precision in complex settings. As shown in the evaluation (see Figure [1. this method demonstrates poor performance compared to other approaches, failing to accurately prioritize the most effective targets. The model takes as input the cell line's multi-omics features and outputs a ranked report of candidate targets, but its statistical scoring alone is insufficient to capture deeper, context-specific biological relevance.
GAT As graph-based baseline, we adapt Graph Attention Network (GAT) to assess the ef- feet of gene knockouts using multi-omics data and CRISPR-derived gene effect scores. The input graph represents a biological hierarchy, where nodes correspond to genes, transcripts, and proteins, and edges capture central dogma relationships and known molecular interactions. For each gene of interest, simulate the perturbation process by masking the node corresponding to the gene and recursively masking its downstream elements-nanely its associated transcripts and translated the transcriptional and translational disruptions observed during real gene knockout. This masked subgraph is then passed to the GAT model based on the pretrained model ft (parameterized by 6%r) ), which aggregates information from the unmasked neighborhood to predict the impact of the knockout on cellular viability, matching the gene effect values from the CRISPR dataset. This design allows us to evaluate how well the local subgraph structure and omies context can recover the observed CRISPR perturbation effect. Although this method incorporates the biological topology and omics signals through attention- weighted message passing, it lacks explicit global reasoning for disease mechanism and has low performance CRISPR target prediction.
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G-Retriever G-Retriever is graph language framework that enables conversational interaction with large, complex graphs enriched with textual node and edge attributes. It facilitates question answering by aligning user queries in natural language with the underlying graph structure, re- turning both textual responses and highlighted subgraph evidence. This setup is particularly use- ful for scenarios where graph elements (e.g.. proteins, drugs, phenotypes) are associated with rich descriptions or biomedical annotations. In our implementation, we instantiate G-Retriever with two pretrained components: (1) a language model finit that has been fine-tuned on domain-specific question-ansee answering dasks-specfiiallle encoder based on a pretrained Graph Attention Network (GAT), denoted as f parameterized by &G The GAT is trained to encode signaling pathway graphs and capture topological and contex- tual dependencies between biological entities such as genes, proteins, and molecular interactions. This dual-modale modality initialization, textual reasoning via finit and structural embedding via fe al- lows G-Retriever to support context-aware retrieval over large graphs that exceed the LLM's con- text window. Through this integration, the framework effectively aligns natural language queries with underlying graph structures, enabling biologically grounded reasoning over complex networks. While G-Retriever offers modest performance improvements over conventional GraphRAG base- lines pars-cuuatcllrlyy in terms of contextual relevance and retrieval accuracy- it remains limited in its ability to construct biologically interpretable subgraphs with strong explanatory power. Specifically, pathway-level constraints,
biomedical questions; (2) a graph
G-Retriever lacks mechanism for enforcing structural coherence which are essential for generating subgraphs with high fidelity to known signaling or regulatory cascades. Consequently, its generated outputs often fall short of the domain-specific interpretability and performance achieved by our proposed method, which integrates graph-aware reward signals and multi- step reasoning for robust target identification.
Other Models RoG, SubgraphRAG, and GNN-RAG are Sridenee-uuggrah--deeeeddd methods that share common assumption: a small, clean, and reliable subgraph exists from which retriever module can be trained to provide reasoning paths to the LLM. However, this assumption breaks down in large-scale biomedical knowledge graphs, which are inherently noisier and lack validated reasoning paths. In the Target-QA setting, each question is paired with a one-hop disease-protein subgraph and one-hop PPI neighborhood extracted from BioMedGraphica. These subgraphs are extremely noisy and large (containing thousands of nodes and edges), and lack ground-truth mecha- nistic subgraphs, making supervised retrieval difficult. Specifically, RoG, SubgraphR and GNN- RAG require ground-truth reasoning paths for training. Since such annotations are unavailable in our setting, we employ ChatGPT 5.1 to generate candidate reasoning paths relevant to the annotated cell lines, incorporating disease context and multi-omic profiles. However, this GPT-derived super- vision is inherently noisy and biologically incomplete, limiting retriever effectiveness. All baselines use the fine-tum tune-tuned Llama3-8B -B----rt-- as the backbone LLM.
D.4 RESULTS
We summarize detailed per-cohort results across various cancer types from TCGA in Figure 11 and Tables 46 demonstrating that GALAX achieves the highest performance across nearly all evaluated metrics and cancer cohorts. M2T, serving as baseline relying solely on multi-omics data without any structured graph or language modeling, performs poorly, underscoring the neces- sity of incorporating richer contextual information. Adding graph structure via pretrained Graph Attention Network (GAT) provides some improvements, albeit limited, indicating that static graph representations alone are insufficient for capturing the complex biological interactions inherent in the data. To further elucidate the value of incorporating language models, we evaluate multiple LLaMA3 (L3) variants. Models that lack Target-specific finetuning, such as L3 combined only with omics data (L3) or with additional knowledge graphs (L3 + KG), deliver suboptimal performance, even with external knowledge enrichment. Biomedical-domaiin domain-specifiieeeeeee finetuning (L3-FT(Med)) significantly enhances performance in identifying relevant targets, and integrating knowledge graphs yields modest incremental improvements. Nonetheless, even the variant supervised by Varget-QA (L3-FT(QA) KG) demonstrates limited additional benefit from knowledge graph integration, pri- marily due to its inability to dynamically generate structured subgraphs tailored to the reasoning task. The G-Retriever model, augmenting pretrained graph attention network with retrieval mech- anisms, achieves stronger performance compared to earlier baselines but remains limited by its ab- sence of step-wis wise supervision and interpretability in biological reasoning. In contrast, GALAX uniquely integrates a Target-QA finetuned large language model, a pretrained Graph Attention Net- work, and reinforcement learning-driven subgraph generator guided by graph process reward
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COADHREAD
PAAD
OBM
SABC
OV
Jown
poroce
20ot
SKON
CF
SCUC
IASC
USSC
PROM
DIOSCE 2001
WIN
STAD
Sywe
Syn
P7/ Dea
Cideeer pade
Figure 11: Model performance plots over main Target- -AA datasets
model. This sophisticated architecture facilitates interpretable and biologically informed reasoning by dynamically generating structured subgraphs relevant to the biological context. Consequently, GALAX significantly surpasses all baseline models, achieving otate-of-the-art performance across both overall average metrics and individual cancer cohort-specific evaluations.
D.S GENERALIZATION TO UNSEEN CANCER TYPES
To assess how well GALAX generalizes across cancer types, we constructed three holdout sets in which all cell lines under same TCGA codes were removed during training and used only for evaluation (see Table I2). This design forces the model to generate CRISPR target predictions for cancer types not observed during optimization, providing direct measure of cross-cancer transfer and robustness to unseen molecular contexts.
As expected, performance decreases when entire cancer types are held out, since no fine-tuning data from those cancers is available. Nevertheless, the results in Table [3 show that GALAX maintains stable performance across the three held-out cancer groups. Precision, recall, F1, and Jaccard values remain consistent, with an average Fl of 0.4862 and Hit@ 10 above 0.85. While these scores are lower than the full-data GALAX model, the gap is moderate, indicating that the model retains the ability to infer relevant targets even when the TCGA code of the input cell line has never appeared during training, as long it has been well pretrained with sufficient samples. This demonstrates that GALAX does not overfit to specific cancer labels and can transfer its reasoning across diverse cell-line backgrounds, addressing concerns regarding generalization to new cancer types.
Table 12: Held-out cancer type fold assignments
Holdout Set
Train
Tel
Train/Te TCGA Codes
Hold-oat Cancer TCGA Codes
COADREAD, DLBC, KIRC, LCML, LUSC PRAD, SARC, SKCM, STAD
Holdont Set
238 (65.68)
125 (34.4%)
20/
Holdont Set
289 (79.6%)
74 (20.49))
21/9
BLCA, GBM, LAML, LGG, LIHC
MESO, MM. NB, UCEC
Holdont S
220 (60.6%)
143 (39.4%)
21/9
ALL. BRCA, ESCA, HNNSC LUAD OY. SCLC, THCA
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Table 13: GALAX performance unseen cancer types under held-out evaluation
Holdout Set
Precision
Recall
F1
Jaccard
Hit@5
Hit@ 10
Holdout Set
0.4931
0.4501
0.4899
0.3100
0.9024
0.8688
Heldout Set
0.5124
0.3485
0.8581
Holdout Set
0,4832
0.4715
0.4765
0.3161
0.8993
0.8448
Average
0.1962
0.1789
0,4562
0.3242
0.8907
0.8372
GALAX
0.5472.o.0m3
0.53992m.044 1.5552100001
0.3726:0.000
0.92492o..m
0.88151
D.6 EXTERNAL DATASETS
To examine generalization bevond Target-QA, we performed an external evaluation on the pediatric cancer dataset from PedDer which contains CRiSPR-based gene effect profiles and cell-line an- notations for 31 pediatric cancer samples. The data were processed following the same steps as Target-QAl QA. Due the limited sample size, we did not train or finetune on this dataset and instead evaluated all models in a zero-shot setting. Given that all samples are cancerous, the M2T baseline cannot be applied. As shown in Table [4] baseline graph models and sanguage-modes variants still have near-7ero performance. RAG yield moderate gains, but GALAX attains the highest precision, recall, F1, and Jaceard scores, along with notable improvements in Hit @5 and @it@ 10 finetuned on Target-QA with multi-omics and disease-related proteins information. These results demonstrate that GALAX can extend its prreetprediction ability to external cancer datasets without retraining, providing strong evidence of its robustness and transferability beyond the Target-QA benchmark.
Sraph-auggmetedd methods such RoG, SubgraphR AG, and GNN-
Table 14: Model performance on PedDep cancer dataset
Model
Precision
1
Recall
F1
Juccard
Hiu@5
Hird 10
GAT
0.000|5=0.000
d.00055e0.00e
+.0005550.0000
D.00U12=00000
0.0000=0.0000
d.0100)sa..00
(.3FFT(Med) Omics
0.0144_g.oo 0.000
0.01 1010
0.0090 1.00000
0.0050-0.000 0014
0.021020.012
0.010910
L3-1T(MMed)> Omics
0.0167
0.0060
0.0068 100009
D.0035_2..0ce
0.0072:0.00.
L3 + Omics
0.0051=0.0L08
0.00011=0.000
0.0018=00.00u =000000
0.0018
D. D.00019=0.008
0.00002a0.00x
(10100)):a.o0
L3 Omics
0.0192=0.00
0.0032+0.0022
0.0050):0.00
D. 0.0026==0.co
0.014222..116
0.02141=0.0r
L3-FT(QA) + Omics
0. 0.26
2.2606
0.2606i=0.0200
0.1517=0.00s2
0.5610=n.sas
(.4521=0..00
L3-FT(QA) Omics
0,2619=0.000
0.2552e0.00st
0.2581
0.1489
0.501820.006
0-4952+00021
G-Rctriever pre-GAT
0. 0.2021
0.2610aa 0006
0,261 77000000
D. 0..022=0.0:23
0.5143en..o 0400
0.4524aa0 0925
RoG
0.2730=0.000
0.2667 1.0
0.2697
0.1 4.15002.0.000
0.51 0.514310.0mm
0.47141
SabgraphRAG
0.2736_..acw:
0.2690
0.27121..00zz
0.1579 .0000o2
0.5619aw.ams
0.5286 100771
GNN-RAG
0.2760=0.0000
0,27001.
0,272810
0.1580
0.571 1.5.041
0.533311
GALAX
0.29 4.0.0r1
0.268010.0m
0.2801 L10.0135
0.1 2.1703....0c ooe.sesss
0.6357 i 10.00
0.5179100513
GALAX (Qwen2.5-7BB)
0.2921io.uns
0. 0.2895
0.2908
0.1708
0. 0.6667:10mm
0.5381
E REINFORCEMENT LEARNING GENERATED SUBGRAPH DETAILS
Figure 12 provides a high-level overview of the explainable subgraphs of part of cell lines, high- lighting key proteins and their predicted functional interactions that define the unique molecular signatures of each cell line. These interpretable network maps identify the most salient features pri- oritized by our model. To move beyond structural insights and assess their biological significance, we conduct enrichment analyses to evaluate whether the identified molecules are significantly over-represented in curated knowledge bases such as KEGG and WikiPathway. This analysis enables to contextualize the salient features within relevant targets and signaling pathways. The resulting cell- interpretations bridge dodel-driven discovery with established biological knowledge and are detailed in the following sections.
E. ENRICHMENT ANALYSIS
Enrichment analysis of sample ACH-000054 (HT-1180), representing fibrosarcoma (metastasis; SARC), revealed significant association with Sarcoma (P-value = 1.625 x 10 Adj.P 4.08 x 10-2) driven by COLIA1, WWTR1, APAF1, PLK1, EPHB4, and SH2B1. Pathway- level signals underscored apoptotic and stress-response programs, with strong enrichment of Apoptosis Modulation by HSP70 (WP384; P-value = 1.864 x 10-6, Adj.P 1.45 10-4) and Apoptosis (WP254; P-value
1.73 x 10-4, Adj.P 5.26 x 10 3), each supported by
httts::/peddep.rrre
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Protein Signaling Networks Across Different Cancer Types
SPCA (4CH-560654)
(ACH-0000011
SKOM (ACH-6000199
ALL 060676
MISO (4O--86652)
PWN (ACH-0998571
KBBC (ACH-000099)
BLCA (ACH-000010)
uCEC (404-033864
Figure 12: Part of explainable disease mechanism by core signaling subgraphs
MAPK10. APAF1, and TNERSFIA. Additional inflammatory and mitogenic axes were indicated by TNF alpha Signaling Pathway (WP231; P-value 2.27 10-4, Adj.P 5.26 10-3) involving APAF1, PIK1, and TNFRSFIA, and by the MAPK Signaling Pathway (WP382; P-value 2.70 x 10-1, Adj.P = 5.26 10-3) featuring MAPK10, DUSPI, RASGRF2, and TNFRSF1A A. Finally, Nanoparticle-mediate activation of receptor signaling (WP2643; P-value 6.02 x 10 Adj.P = 8.99 x 10-$ highlighted extracellular matrix and receptor-proxima cues via COLIA1 1 and MAPK10. Collectively, recurrent involvement of apoptosis regulators (APAFI, TNF,SFIA), MAPK components (MAPK10, DUSP1, RASGRF2). and proliferative drivers (PLK1) points to coordinated apoptotic, inflammatory, and receptor reCP signaling programs characteristic of fibrosarcoma biology.
For sample ACH-000001 (NIH:OVAAR-)) OV), analysis indicated only a weak disease-level association with ovarian cancer (P-value 1..1 x 10-1, Adj.P 1.65 x 10-1), primarily linked to ERBB2. While this signal was not significant after multiple-testing correction, pathway-level evaluation revealed several strongly dysregulated processes. The Prolactin Signaling Pathway (WP2037: P-value
ovarian adenocarcinoma line (metastasis;
2.90 10-4
Adj.P= 1.49 x 10-2) was prominently enriched through RPS6KB1, NOS2, and ERBB2, pointing to growth dector-driven oncogenic signaling. Similarly, enrichment of the Leptin Signaling Pathway (WP2034; P-value = 2.90 x 10 Adj.P = 1.49 x 10 10-2) and the ErbB Signaling Pathway (WP673; P-value 4.92 x 10-1 Adj.P = 1.69 x 10-2) implicated RPS6KB1, ERBB2, and PLCG2, reflecting interconnected receptor tyrosine kinase and metabolic networks. Additional pathways included IGF1-Akt signaling (WP3850: P-value = 1.26 X 10-3, Adj.P 3.09 x 10 10-2) driven by RPS6KB1 and TNESE9, and BDNF-TrkB Signaling (WP3676; P-value = 1.52 x 10 3 Adj.P = 3.09 x 10-2) 2) via ARC and RPS6KB1. These results underscore the central role of ERBB2- meddmttdd receptor activity and downstream RPS6KB1-linked pathways, consistent with known mechanisms of ovarian carcinoma progression.
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In sample ACH-000219 (A-375), representing amelanotic melanoma (primary; SKCM), enrich- ment testing identified a highly sigmificant signal for melanoma (P-value = 1.08 x 10 6 10-6, A 1.59 x 10 +) supported by broad gene panel including CCL25, STAT5B. HSP90AAL. SER- PINB1, VWF, TYR, THBS1, RELA, TJPI, GHR, CDH5, IRF1, TNESF10, TRIM24, NCAM1, ATG7, TNERSF4, and 11.9. Immune and inflammatory cascades dominated the pathway enrichments, with the NOD Pathway (WP1433; P-value = 1.77 x 10-6, Adj.P = 2.28 x 10-*) enriched via HSP90AAI, NLRP12, MEFV, and RELA, pointing innate immune activation. Cytokine-related processes were also evident, including the II.4 Signaling Pathway (WP395; P-value 5.42 x 10 Adj.P= 2.65 x 10-4) and the IL-9 Signaling Pathway (WP22: P-value 6.16 x 10 Adj.P 2.65 x 10 10"*), with contributions from STATSB, PIK3RI and IL9R, consistent with Th2/Th9 regulation. The Oncostatin M Signaling Pathway (WP2374; P-value 1.14 x 10-5, Adj.P 3.60 x 10-1) further implicated STATSB, TYK2, PIK3R1, and RELA, linking Crokinc-STAT and NF-xB signaling to melanoma progression. Overall, repeated enrichment of STAT5B, RELA, and PIK3R1 across pathways highlights coordinated dysregulation of inflammatory and cytokine signaling networks in amelanotic melanoma.
Enrichment analysis of sample ACH-000070 (697), representing B-cell acute lymphoblastic leukemia (ALL) with the (21:193 E2A-PBXI (TCF3-PBXI) translocation, revealed a strong diseasc-level association with acute lymphocytic leukemia (P-value 7.37 x 10-5, Adj.P = 8.10 x 10-3) supported by genes including IGF2, WBPIL, ELSPBP1, HOXD4, TCF3, THY1, SIRT1, GATA1, and PBX1. Pathway enrichment further emphasized leukemia-relevant transcrip- tional programs: Sudden Infant Death Syndrome (SIDS) Susceptibility Pathways (WP706; P-value 3.02 x 10-4, Adj.P 1.94 10-2) through TCF3, POU2F2, ESR2, and PBXI; and the Wnt/3-catenin Signaling Pathway in Leukemia (WP3658; P-value = 1.29 x 10-3
Adj.P 4.13 10-2) involving AXIN2 and TCF3. Additional signals included the Breast Cancer Pathway (WP4262; P-value = 3.85 x 10 10-3,Adj Adj.P = 6.06 x 10 3) with E2F3, AXIN2, and ESR2, and the broader Wnt Signaling Pathway (WP363; P-value
5.10 x 10-3, Adj.P 6.06 x 10-2) featuring AXIN2 and TCF3. Collectively, these findings underscore recurrent involvement of the TCF3-PBX1 fusion together with Wnt/3-catenin signaling and leukemogenic transcription factors, consistent with the molecular etiology of this ALL subtype.
Enrichment analysis of sample ACH-000092 (NCI-H2452), representing pleural mesothelioma (metastasis; MESO), showed modest disease-level association with Lung Neoplasms (P-value = 4.965 x 10-3 Adj.P = 1.89 x 10-1) supported by MAP2K1, SLC26A2, AIFM1, SP1, ATG4B, SATI, GLUL, and PPPIRYB. Pathway analysis highlighted metabolic and growth- factor-linked programs: Amino Acid metabolism (WP3925; P-value = 1.20 x 10-3, Adj.P 5.63 10-2) involving BHMT, PDHA1, and GLUL; the Estrogen signaling pathway (WP712: P-value = 1.27 x 10-3, Adj.P = 5.63 x 10-2) marked by MAP2K1 and SP1; and TGF-3 Signaling (WP366; P-value 3.47 10-3 3 Adj.P 1.15 10-2) featuring MAP2KI,
SUMO1, and SP1. Together, these results suggest coordinated amino acid metabolic rewiring and transcriptional control via MAPK-SI SP1 and TGF-3 axes in the mesothelioma context.
Enrichment analysis of sample ACH-000817 (RPMI 8226), representing plasma cell myeloma (primary; MM), revealed a highly sigmificant association with Multiple Myeloma (P-value = 3.891 x 10 Adj.P = 6.64 x 10-4) supported by ITGB1, KMT2D, CBX7, TNERSF13B, IDH1, LAPTM5, HLA-C, TNFRSF10A, PIK3R2, KIR3DL1. BHLHA15, PIK3R1, MEFV, AURKA, NRAS, NUAK1, NPC1, BTK, KRAS, SGK1, HRAS, MAPK3, and FBXO9. Pathway analysis further highlighted the ErbB Signaling Pathway (WP673; P-value = 8.47 x 10-8, Adj.P 1.03 x 10-5) involving MAPK10, CAMK2D, NRAS, PIK3R2, KRAS, PIK3R1, HRAS, and MAPK3, indicating convergence of RAS-MAPK and PI3K effector cascades downstream of ErbB family receptors- fecttrrss-eaturrs consistent with mycloma signaling dependencies and potential therapeutic vulnerabilities.
Enrichment analysis of sample ACH-000649 (786-O), representing renal cell carcinoma (primary: KIRC), revealed a highly significant association with Conventional (Clear Cell) Renal Cell Carcinoma (P-value 1.273 10-9, Adj.P 9.68 x 10-7) supported by TGFB1, IL4R, VCAMI, APAF1, OGG1, MSGN1, MITF, UNC5C, SMARCA2. TNF, POMC, FPGT, ORC2, ERBB2, ALDHIAI, PGK1, BIRCS, ZNF536, VHL, and CRYAB. This gene spans
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hallmark features of clear cell RCC biology, including hypoxia/VHL signaling (VHL, PGK1), apoptosis regulation (APAF1, BIRC5). growth factor and RTK pathways (ERBB2, TGFB1), cammune-inflammaane components (TNF, ILAR. VCAM1), metabolic and oxidative stress sponses (ALDHIA1, CRYAB), and genome maintenance/chromatii remodeling (OGG1, ORC2, SMARCA2), collectively underscoring a prototypical clear cell transcriptomic signature.
Enrichment analysis of sample ACH-000018 (T24), representing bladder carcinoma (primary; BLCA), showed borderline disease-level signal for Cancer (P-value = 5.755 x 10 Adj.P 1.02 x 10-1) driven by ZBTB16 and RAF1. Pathway-level analysis revealed significant
enrichment of Focal Adhesion (WP306; P-value
1.17 x 10-1, Adj.P
1.03 x 10-3)
involving MAPK10, RAPIA, COL4A6, and RAFI; the ErbB Signaling Pathway (WP673; P-value 2.20 10TH Adj.P 1.03 10 10-2) 2) featuring MAPK10, CAMK2D, and RAF1: and
Indegrin Cell Adhesion (WP185; P-value 2.99 x 10-1, Adj.P P = 1.03 x 10-2)
with MAPK10, RAPIA, and RAF1. Collectively, recurrent involvement of RAF1 together with MAPKI0 across adhesion and ErbB-axis pathways points to convergent RTK-MAPK and integrin signaling programs in this BLCA context.
Enrichment analysis of sample ACH-000864 (COLO 684), representing endometrial adenocarci- noma (primary; UCEC), revealed highly significant disease-level association with Carcinoma, Small Cell (P-value = 1.064 x 10-6, Adj.P = 1.29 x 10-2) supported by SMARCB1, AKT3, MTOR, and SMARCA4. Pathway analysis highlighted potent receptor tyrosine kinase and stemness programs: the ErbB Signaling Pathway (WP673; P-value 3.38 10-9, Adj.P 4.90 x 10-3) involving JUN, CAMK2A, AKT3, PIK3R2, and MTOR; the EGF/EGFR Signaling Pathway (WP437; P-value = 6.19 10 Adj.P 4.48 x 10-9) through JUN, GJA1, CAMK2A, PIK3R2, and MTOR; and ESC Pluripotency Pathways (WP3931; P-value 4.97 x 10-9) marked by JUN, AKT3, PIK3R2, and MTOR. Collectively, recurrent involvement of AKT3 and MivoR-together with PIK3R2, JUN, and CAMK2A-poinl convergent
1.03 10-6 Adj.P-
signaling and pluripotency-
associated programs in this UCEC context.
E.2 HUMAN EVALUDATION AND LLM JUDGE
To further assess the biological relevance the generated subgraphs, we conducted a comprehensive evaluation involving both human domain experts and Large Language Models (LLMs). The human evaluation panel consisted of three bioinformaticians, denoted as h1 h2, and h3- In parallel, we used two advanced LLMS as automated evaluators (mı m2): ChatGPT-551 and Gemini-3.0 Pro. Both the human experts and the LLMs were provided with the generated subgraphs, corresponding gene and pathway enrichment analysis results facilitate the assessment of biological plausibility.
Table 15: Human Evaluation Scores
Sample ID
TCGA Code
h
hg
hy
mi
Mcan +: Std
ACH-000860)
LUAD
5
5
5
5
4.80
0.45
ACH-000054
SRCA
5
3
4
3,80
0.84
ACH 000001
OV
3
3
2
4
2
2,80
+ 0.84
ACH-000219
SKCM
2
3.60
ACH 000070
ALL
5
5
5
4
4,80 1: 0.45
ACH-000092
MESO
3
3
2
3
3
2.80 1: 0.45
ACH 0008000117
MM
5
3
4.20
ACH-0006449
KIRC
4
4
5
5
4.60 : 0.55
ACH-000018
BLCA
3
3
3
3.00 1: 0.00
ACH-000864
UCBC
2
4
3.20 0.84
Overall
3.7
4.2
3.4
3.9
3.6
3.76 4t 0.80
Each expert independently reviewed ten subgraph examples shown in Table I5blc|5]ame and assigned a score from to 5 based on the degree of correspondence between the subgraph and the known biology of the associated TCGA cancer type. The scoring rubric was defined as follows:
5: Highly related strong and clear match hallmark pathways and well-established features of the cancer type.
4: Related clear relationship to the cancer type but less comprehensive slightly mixed.
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3: Moderately related some relevant elements present, but mixed with non-specific find- ings.
2: Not related - only weak or indirect connection to the cancer type.
1: Not related at all meaningful alignment with the biology of the cancer type.
As shown in Table 15] several samples were evaluated as related or highly related their TCGA cancer types, indicating that the subgraphs generated by the model capture meaningful molecular features. The LUAD sample ACH-000860 received one of the highest average scores, as its sub- graph highlighted pathways such as regulation of phosphatidylinosit 3- 3-kinase signaling and posi- tive regulation of kinase activity, involving genes like EGFR, PTK2, and EPHB4 that are central to lung adenocarcinoma biology. The ALL sample ACH-000070 was also rated highly due the pres- ence of TCF3 and PBX1, whose fusion is a well-established driver event in Acute Lymphoblastic Leukemia.
Table 16: Model Overall performance on Target-QA
Model
Precision
1
Recall
F1
Jaccard 1
Hit@5 1
Hite 10 f
M2T
0.0016
0.0011
0.0013
0.0006
0.0000
0.0029
GAT
0.0006=0.0000
0.0006go o000
0.0006:...000 0000
0.0003=0.0co0
0.0000g 0000
0.0000 0000
L3
0.0071=0.0052
0.0013e00.00z
0.0011=00.001
0.0012=0.005
0.0022
L3 Omics + KG
0.0125=0.00
0.0029:00.00 0003
0.00:13:00000
000g
D. 0.0022=0.oc0)
0.008G=n.008 0057
0.0122+00.r o938
(.3-F((Med) Omics
0.0179 0044
0.013331..00
0.01 15:1
0.0050
0.01 1620
0.0122::0
(3-FT(Med))+ + Omies KG
0.0158_ 0..000
0.005810.0011
16.074
0.0038 _000000
0.01061w.a0w
0.0132 oowwt
L3-FT(QA) Omies
0.5250:=0.0az
0.888920.ous
0.869320.0157
L3-IT(QA)) Omics + KG
0.5185_00.000
0.4
0.5038 Lou0ser
D.3353_...m
0.87941v.00xx
0.8529100153
Retriever pre GAT
0.4763_0.0004
0.392910.c0s
0,4286
0:2757 Lo.0oss
0..004
1.8550
RoGi
0.5208=0.0154
0.472620.0445
0.4924=s..025
0.33338=0.c227
0.85033=0.001
0.8130)aa.sss
SubgraphRAG
0. 12280=0.0044
0
0, 0.1860+000000 0038
0. 0.3269==0.0044
1.6611600010
0,8176+2
GNN-RAG
0.5258 o1 as
0.4735:10
0.4935
0.3345 o...o..03.4
0.865020
0.8323 0905
GALAX
0.5472:00000s
0.33322000011
003726=0..007
0. 0.9249=2.008
0.8815
GALAX (Qwen2.5-78)
0. 0.54115=0.0114
0. 0.5-155=0000 0100
0. 0.54222:00004
0.37
0.907920.00
0.004
0.8841=00 0120
Table 17: Model performance on LUAD
Model
Precision
Recall f
FI f
Jaccard
Hice:5
Hir 10
M2T
0.00200
0:0014
0.0017
0.0008
0,0000
0.0000
GAT
0.0000=0.co00
0.0000=0.0000
0.0000000.000
0.0000s2..000
0.0000
D. 0,0000s0..000 0000
L3 Omies
0.0079
0.0005-00000
0.0009200006
0.0006523.000
0.009G 00os
D. 0.0048 0052
1.3 Omics KG
0.00142g.com
0.0010_0.001
0.001 1000000
0.000612.0000
0.0000 0000
0.0000
L3-FT(Med) Omies
0.0091=0.cols
0.0105=000044
0.0079500002
0.00-10:3.0011
0.00000=40
0.0000++0.0o
1.3-PT(Med) + Omiics + KG
0.0061 c07
0.0024_.00016
0.0025::00000
0.0013:2.000
0.0006 o 65
0.00448 0090
L3-FTICRISPR)+ Omiess
0.5201=0.c40e
0.1903=000632
0.5015
0.3396=2..0ss
0,8176 0776000.003
D. 0.860T+900018
1.3FTTCRISPRR+ Omiics KG
0.5214220.042
0.507320
0.341622. 0314
0.7905 0196
0. 0.804922..00 0541
G-Retriever pre-GAT
01414
0.3881 0054
0.2671 o18s
0.8807 as 0000
0.85244...0
RoG
0.5213=0.c227
0.1562=00.054
0.17
0.3228 0590
0,8095
82888+000218
SubgraphRAG GNN-RAG
0.5
0.4-48-00
0. 027
0.311422.zz2 0287
0.8190aac 0:65
0 00.8238 0092
(.5533L=0.c25
0.50
0.5165200103
0.35663e:.0112
0.7905ee00011
0. 0.7571+0.0021
GALAX
0.53/5=0.c188
0.5157=0.00s
0.5217=0000
=.3581=2.000
0. 0..2338-a 0498
0. 1..80=000002
GALAX (Qwen2,5-7B)
0.5-475:0
0.5466250.011
0.54665:0 0060
0 0.3778:00004
0,90 0:06
0. 0.800770.000
Table 18: Model performance on BRCA
Model
Precision
Recall
FI ?
Jaccard
Hic@
Hit@ 10
M2T
0.0000
0:0000
0.0000
0.0000
0.0000
0.00000
GAT
0.0033=00coop
0.00
0.0053e0.0000
0.0017
0.0000
0. 0.9000++9.0000
L3 Omies
0.0020=00.oas
0.0017=00009
0.001850000:
0.000982..016
0.0000 0000
D. 0.00000+eo.o 0000
1,3 Omics KG
0.007320.cos coos
0.0033-00000
0009
0.004420
0.00222200019
0.0111
D. 0.00G0-40.000
L3-FT(Med) Omicc
0.0110=0.cee
0.0106==0.0075
0.00
0.0011==30008
0.00000
0.0111+0.0152
L3-FT(Medi + Omiis + KG
0.01 20077
000000=000000
0.0071
000010000013
0.0036:22.00m ocos
0.00000a4 0003
0.01 11 0192
L3-FTICRISPR)+ Omnics
0.50)74
0. 19552200035
0.3336=22.00z
0. 0.8889 0809
D. 83880+900000
L3-FT(CRISPR)+ Omics KG
0.485G=00com5
0. 0.4656-0 0436
0.4751
1.31-2223.0664
0,8778aac 0..2
0.82224..0o 0347
G-Retriever pre-GAT
0-4414=0.comp
0.3772=00010
O. 0062
0.2007=33.0o
0,8087 0000
0 S667 0000
0.4791 =0.007
0.4311=00121
0.2999
0.7667
0.7611+0.1118
SubgraphRAG GNN- RAG
(.1708=0.cam
0.3917=000877
0. 058966200899
0.27/2=20.077
0,7356ies 9989
0. 0.7333 1014
0.4777 20.cas
0.4389=0.0s44
0. 454
0.5025
0,8464ac.us5
D. +9.22.0.074
GALAX
0. 0.5608 00.0
0.5533 oona
0.556922 o028
0.3 0.3886 0022
0. 8889 o 0859
0. 0.8501)+4.0441
GALAX (Qwen2,5-7B)
=.5
0.5206=000419
0.5188000445
0.3532243.0892
0,8556
0. 0,8000049.044
30

---

Published as a conference paper at ICLR 2026
Table 19: Model performance on COAD/READ
Model
Precision
Recall t
F
Jaccard 1
Hic@ T
Hit@l 10
M2T
0,0022
0:0017
0.0019
0.0010
0,0000
0,0000
GAT
0.0000.0.000
0.0000_..0000
0.00001..0000
0.00001a..000
0.0000 0000
0.000010.000 0000
1.3 Omics
0.0233L0.c85R
0.0020
1.003
0.001810.007
0.0133
0.0067
L33 + Omics KG
0.0065=9.0065
0.0033-000001
0001
0.0011+000000 00:000
0.0021=2.0000
0.0000)ac 0000
0.0000+a0.00 0000
1.3.PT(Med) + Omics
0.0300g..com
0.0220
0.01192.00011
0.0104
0.0267 2: 0163
0.0333 0006
L3-FT(Med Omics
0.001H=o.cos
0.0033=0.0001
0.0032+0.000
0.0016:3.004
0,00000:3c.
0.0000)+00.00
1.3-PTCCRISPR) - Omies
0.501 cass
0.4
0. 0.4862200000
0.32200a 0394
0.8400 aso3
0.8267 a. dene
L3-FTICRISPR)+ Omics KG
0.5116=p.c210
0.1807=000040
0. 0.5500000003
+.3001
0,8800
D. 0.83535e0.0251
G-Retriever + pre-GAT
0:4:17=000006
0.3
0.3
0.250Me2.0002
0.72000 0000
D. 0.70077=0000 16
RoG
0.5184:0.co
0.4627-00000
0. 0660
0.3254422.551
0,8033an 0611
D. 008000000.000 04000
SubgraphRAG
0.5306==0.oo0
0.4627=0.0544
0.187L=0.066
0.32:3::3.047
93933300..o 0281
8533++000115
GNN-RAG
D. 0.538820.o0 0306
0 004667200043
0. 0.
0.3296-22.0502 0302
0.8533ee 0402
0 8867=-0.0115
GALAX
0.5272:0.0148
0.5120=0
0.5192:00
0.351 0.35122..00
0.92200
0.8400aa
GALAX (Qwea2.5-7B)
0.5177=0.024
0.5187 0061
0.5177=00008
0.3498=23.009
0,9200))accoom 0500
0.8733e0.0000
Table 20: Model performance on PAAD
Model
Precision
Rocall
FL
Jaccard
5
Hit@ 10 7
M2T
0.0017
0,0013
0.0014
0.0008
0,0300
0,0000
GAT
0.00255-0000
g.0025
0.0013ee.0o00
0.0000) (000
2.0000)as.oom
L3 + Omics
a.00000=n.c0m
0.00000=0.000
0.00000sa.0000
0.003Dea..ooe
0.00000=sc.000
D. 0.8000)+...000
L3 Omics KG
0.0139=00.241
0.00008-000044
0.0016200022
0.0008=2..014
0.0167 0289
0. 0.0083++0.00 0144
L3-FTIMed) + Omicss
(100151 su.owe
0.0050
0,003 20.ude
0.0020a
0.0167 2:0
D. 0.80883
L3-FT(Med) Omics
0.000650..o0s
0.000080000014
0.000/6e0.0000
0.00033-2.000
0.0001)::00.0 0000
0.80000++0.0000
13--TTCCRISPR)+ Omies
1.56449.0cc00
0.5355 0..:58_.0.0ess
0.5492
0,3811 0,3811 2000004
0.8000) uSt00
0.8417
L3-FTICRISPR)+ Omnes KG
0.5:59=00.070
0.5183
0.5330e0.066
0.9657=000000
0.8333
D. 0.8530
G-Retriever + pre-GAT
0.55PL=0.1144
0.1367= 0008
0.501620.000
0.3387 13387=0..oo
0.8500) (0000
0.8500)+000000 0000
RoG
0.5196.00.222
0.4842 0.141
1..9973.01100
0.3462 130177
0,7667 1726
0.7833 10.1101
SubgraphRAG
0.5873=0.0000
0.3308=000002
0.5518
0.38609ee..0cs
0.8833-0..m 0289
0.8750=0.0000
GNN RAG
0.4923
0.4283 00000
0.4522
0.3088 Lou0zox
0.7067 1463
0.791 100
GALAX
0.570810.01
0.5739
0.4044 10.154
L.8333
0. 10.50000.000
GALAX (Qwen2.5-7B)
0.5394=0.041
0.5
0.5387 500042
0.3718
0.8167sc.0299
0.8333ee0.0:44
Table 21: Model performance on GBM
Model
Precision
Recall
F1 1
Jaccard
Hicel
Hiu@ 10
M2T
0.0000
0:0000
0.0000
0.0000
0,0000
0.0000
GAT
0.0000_n.cw0
0.0000_ 00000
0.00000
0.00001a.0000
0.0000
0.000010.000
1.3 Omicc
0.0055 go.om
0.0011-0amp
0.0019
0.0009
1.0000
10.000
Omics KG
0.0000.0.c000
0.0000_00000 0000
0.0000100000
0.0000 0000
0.0000 0000
0.0000.... 0000
L3-PT(Med)) + Omncss
0.0069 19.099
0.0133_ o.0ss
0.0090
0.004610.000
0.0030 100000000000 10000
0.0000
1.3-TT(Med) Omics KG
0.0107=0.0158
0.0056-00000
0.0064200004
0.0032:0.000 0007
0.00000:20.00
0.000011..0
1.3-TTCCRISPR) + Omics
0.5641 Loocone
0.53333 0.0022
0.5480
0.37941o.mor
0.933331..0077
0.9000
L33FTTCCRISRR+ Onnics KG
0.5391=0.cos
0.51782000144
0.5251
0.3596:23.006
0 0.9656=20oom 0.05
D. 0.8446430.00m
G-Retriever + pre-GAT
0.490620.c0ms
0.4411-00000
0.4642200010
0:3037 0000
O 0.9556:20 08e5
0383333 0377
RoG
0.5910.1o.2a 0281
0.5078_.0.110
0.538010
0.3734
0. 0.9556 14.56....0700 07770
0. 0.9444-_o _..00 00.050
SubgraphRAG
0.5066=0.co77
0.454=0..00 1001
0.4
0.3176620.0 0729
0,77788a6
D. 0.75566=00.0
GNN-RAG
1.5567200ccm
0.54
0.55211.0020 10001
0.38561..00-
0.8087.16 1.:77:00-3000 9009
0.81 11,00. 1540
GALAX
0.5527 g0.om
0.5422_.
0.5473.000er
0.376010.0079
1.
0.8667
GALAX (Qwen2.5-7B)
0.5715=20.222
0.565620.00 0280
0.5685 0240
0..398200000 0200
0.9011
O. D.8556600.0 0385
Table 22: Model performance on SARC
Model
Precision
Recall f
F
Jaccard 1
Hir@5 f
Hit@r 10
M2T
0. 0.0047
0:0033
0.0030
0.0019
0.0000
0.0000
GAT
0.0000=a.coo
0.0000=000000
0.00000=00000
0.0030ee..c00
0.000 0000
0.900 0000
L3 + Omics
0.0111 =0.102
0.0033=00000
0.0051+000005
0.0026:22.045
0.00000 0000
D. 0.9000):+0.0oo 0000
1,3 Omies KG
0.0271 c000
0.0111200000
0.0157200000 0009
0.008023.000 0015
0.0222 6385
0.02222+4.. 01
L3-FTiMed) + Omics
0.0167=00115
0.0229:0.1800
0.0119he0.0004
0. 0.0222 0383
0. 0.06667+00.00
L3-FT(Mesd) Omics KG
0.0270=0.cios
0.0089-00001
0.0120+00000
0.0061 10 0005
0.0222
0.0222++.00
L3-FTTCCRIPRR Omies
0.5089-00s55
0.5211
0.3347
0. 0.9111-s 10 0580
0.8778=s.0.52
L3-FTTCRISPR)+ Omiics KG
0.5123=00co07
0,50
0.5207
0.3570e3.0064
0,8101
0.8111130.0s5
G-Retriever + pre-GAT
0.4461 =0.00s
003667
0. 000
0.2518 0003
D 8,8007 0000
0. 1292222.00 0100
RoG
0,5533 0201
0.4589=00000
0. 0.1922200413
0:3824:0.0075
m1883 0.889944
0.8778 777...0.000
SubgraphRAG
0.5365=0.c277
0.4/15==001s
0.1772400185
0.3175==3.0006
0.8889400
D.560T
GNN-RAG
1.5485=m.comm
0.44778.uused
0.3426::0.ums
0,8889 usss
0.588 20.8
GALAX
1.5414
0.5144=0000s
0.527 0000157
0.96022...mms
0. 0.9111=0.100
D. 0.844419..0m
GALAX (Qwen2.5-7B)
0.5383=0.c00
0.5311+0.om 0084
0.5317=0 0090
0. 0.36741 00774=0.000 0088
0 0.9111=0.100
0.568940..00 0034
31

---

Published as a conference paper at ICLR 2026
Table 23: Model performance on OV
Model
Precision
Recall f
F
Jaccard 1
5 1
Hit@l 10
M2T
0,0000
0,0000
0.0000
0.0000
0,0000
0,0000
GAT
0.000020.c000
0.0000_..0000
0.0000
0.00001a.0000
0.0000 0003
0.000010.000 0000
1.3 Omics
0.0241 g
0.0044_
1.0075
1.0038
1..000
0.01
L33 + Omics KG
0.0660=0.co67
0.0111=00102
0.0183:00101
0.0083+2.000 0082
0.0000) 0000
0.0689sao.oo 0770
1.3.PT(Med) + Omics
0.035620.041
0.0100_.0.007
0.010220
0.0051 1:0 000s
0.045420 0770
0.0222 0015
L3-FT(Med Omics
=0.090
0.0311-0.0200
0.0157=00575
0.0253=3.022 0226
0,00000:c..00
0.0533++0.008
L.3-PTCCRISPR) - Omies
2.5440900.comm
0.51672000:06
0.3631 2:3 orn5
1. 1,0000: 0000
0.91 10:111,00000 0064
L3-FTICRISPR)+ Omics KG
0.5139=00.o00
0.4989-00.015
0.5200e0.:125
0.35/12e000000
0,8556+40.085
0,3 0.9:11160.0055
G-Retriever + pre-GAT
0.5:13=00.025
0. 015
0:1575
0.2991=2.0041
1 1.000000.ooo 0000
O. 1699667-0om 0000
RoG
0.5708800.155
0.541320
0.377442.001
0.9350ias 0385
D. 9.22224.0008 0355
SubgraphRAG
0.61 0.6113++0000 0414
0.4833=0.054
0.5289=0.025
0.36500=3.0188
0,9333
0.9:141e0.00s5
GNN-RAG
0.5687=0.com
0.46000..:055
0. 0.00
0.3380 ocas
0.8444a6, 1540
0. 11 1640
GALAX
0.5460
0.5344-0
0.371422.000 0.5
0,8444ac
GALAX (Qwea2.5-7B)
0.5613=0..100
0.5689#0.00 0000
0.5651:0o 08ss
0 0.3957=0.0040
0,9778 038s
0. 1.9566ii0..009
Table 24: Model performance on SKCM
Model
Precision
Rocall
F
Jaccard
5
Hit@ 10
M2T
0.0055
0.0033
0.00:11
0.0021
0,0300
0,0333
GAT
0.0000==..oom
0.00000=0000p
0.00000ga..040
0.00I0ea.uao
0.0000) (000
0.0000)as.ooo
L3 + Omics
0.00000=n.com
0.00000=00000
0.00000sa..000
0.000Dea..ooe
D. 0.8000)=s..000
L3 Omics KG
0.00 -0.04
0.0011=00001
0.0016+000045
0.0008+22.0014
0.0000) 0000
0.00 0.0111++0. 0152
L3-FTIMedd + Omiess
(100I47 su.047
1.00189:0.000
0,0061 20.002
0.00%1 squsses
0.00010 10000
0.50000
L3-FT(Med) Omics
0.0000=0.0000
0.0000000.0000
0.0000000.0000
0.00300=2.0000
0,0001)==0
0.8000+++0.000
L3-FTTCCRISPR) Omies
0.5145
0.4922
0.5028100043
0.3399
0.84441c. 1.364
1.855611..nss
L3-FTICRISPR)+ Omnes KG
0.4
0.0.71
0.2161:200511
008087
G-Retriever + pre-GAT
0.5123=9.064
0.1100=00000 0005
0.2958 022222
0,9833 0000
0.8778=s.ous
RoG
0.5201 guucass
0.4522
0.001
0.4797 1000500
1.30791
0,80010 2200
0.8111 10.100
SubgraphRAG
0..029
0.43012
0.2929-e0.000
0,6889 2143
0.7222+002141
GNN RAG
0.5258 joucars
0.49566_
Lo.0092
1.344771o.0r0
0.9556 Le.uuss
0.9222 10.025
GALAX
0.5185_0.6116
0.5233
0.5307 1000000
0.3618
1.00000
0.9667_
GALAX (Qwen2.5-7B)
0,5651 100000
0.5589 0280
0.5619 0245
0. 0.3938::0.2213
0,8880sc.or7
0.9111+0.mmz
Table 25: Model performance on ESCA
Model
Precision
Recall
F1 1
Jaccard
Hicel
Hiuee 10
M2T
0.0000
0.0000
0.0000
0.0000
0,0000
0.0000
GAT
0.0000_n.cw0
1.0000_
0.00000
0.00001a.0000
0.0000
0.000010.000
1.3 Omiccs
0.0000=g.com
0.0000_00000
0.000000000000
0.0000
1.0000
10.000
Omics KG
0.0000=0.co00
0.0000_000000
100001000000
0.000020.00m 0000
0.0000 0000
0.0000.... 0000
L3-PT(Med)) + Omncss
0.0143
0.0178_
0.0139
0.0071 1000011
0,0030
10000000
0.0000 1..0000...000m
1.3-TT(Med) Omics KG
0.000020.000
0.0000-000000
0.0000
0.000012.0000 0000
0.00000:30.00
0.000011..00
1.3-TTCCRISPR) + Omics
0.521010.c6000
0.4389 00.00
0.3438 Lo.057
0.93333
0.9333 1..31.0.000
L33FTTCCRISRR+ Onnics KG
0.4878=000001
0.500
0.33-66:2.0000
0,9111 0385
D. 0.9333340.000 0000
G-Retriever pre-GAT
0.508320:0142
0.4156-00000
0.4350220000 0006
0,2950 0064
0.9778aa 0385
0.944442..oo 0385
RoG
0.5275
0.4622_
0.4877
0.3262 Lou035
0.9111 1. u770
0.9558
SubgraphRAG
0.5373=0.co48
0.4744-00052
020
0.3386=20.02 0210
0,8889
0.9333220.0m
GNN-RAG
0.540620.c00
0.5067_00::0
0.51 1
1. 1.0000 o 000n
0.9778_ 0.092
GALAX
0.5775_10.omm
0.5667
0.5720
0,4031 02
0,9556
0.9667
GALAX (Qwen2.5-7B)
0.5706==.c228
0. 014677200046
0. 0.5634
0.3936 0166
0,9333a6. 1163
1.933336000o 0662
Table 26: Model performance on SCLC
Model
Precision
Recall f
F
Jaccard f
Hir@ ?
Hit@r 10
M2T
0.0000
0,0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000=n..ooo
0.0000==00000
0.00000=0.0000
0.00IDee..c00
0.000 0000
0.900 0000
L3 + Omics
0.0000==n.coo
0.00000000000
0.000
0.0000e2..000
0.00000 0000
D. 0.9000):+0,.oo 0000
1,3 Omies KG
0.0000.0.com
0.0000=000000
0.0000100000 0000
0.000023.000 0000
0.0000 0000
0.0000 0000
L3-FTiMed) + Omics
1.0151=0.:212
0.0389=0044
0.02 0220
0.0151 e0.0103
0.00000
0000
0 0.00001=+0.0000
L3-FT(Mesd) Omics KG
0.0000=0.c000
0.0000=00000
0.0000000000
0.0000=3.000 0000
0.00000:c..00
0.0000++0.00
L3-FTTCCRIPRR Omies
L.5337=00c54
0.4989=00479
0.51 055
0.3476xs0.054
0,8087
1158
0.8556:20.000
L3-FTTCRISPR)+ Omiic KG
0.1900=0.coo7
0.4667=000077
0.177820.065
0.3160e22.0565
0,9333ec0 0607
0,877849.000
G-Retriever + pre-GAT
0. =..208=0.coos
0,3910200180 0.3867=0011
0.213422.0100
0,8007 0000
0,8000040.00 0000
RoG
(L497] 20.1477
0.
0.46889=0.1555
0.1786 1300
0.3513=o.uor4 1074
0,7778 2014
D. 0,5000l
SubgraphRAG
0.5516=0.cce0
0.4378=0.171
0.33-16=3.118
88888900
1-0.110
GNN-RAG
L.55003=m.otm
0.4844=0.1773
0. 0.5096 13w6
0.3512
0.8667 mor0
0.5 0.85568aa..uus
GALAX
0.5850
0.5822 0102
0.5836::.00m
0.0043::0.0002
0.9778 0300
0. 000000
GALAX (Qwen2.5-7B)
0.5706==.c190
0 0.5686=00188
0.3998=2.0119
0.9356iec..o 0385
0. 0.9000++..or
32

---

Published as a conference paper at ICLR 2026
Table 27: Model performance on HNSC
Model
Precision
Recall 1
F
Jaccard 1
5 T
Hit@ 10
M2T
0.0000
0,0000
0.0000
0,0000
0,0000
0,0000
GAT
0.0100==a.ooo
0.00000=000000
0.000100a..000
0.00XDeea.ooo
0.0000)asc 0000
0.9000
L3 Omics
0.0:00==p.co0
0.00000=0.0000
0.00000e0.0000
0.0000e23.000
0,0000)=c.00 0000
D. 0.90001:+0,0000
L,3 Omics KG
0.0128 c222
00:07=000009
0.002294 000)
0.001543.000
0.033344 6577
0.0167=+0 0289
L3-FT(Med) + Omnics
0100182
0.0100=000000
0.007990..005
0.00dDese..cos
0.000 (0000
0.00001=+s..000
L3-FT(Med Omics
0.0000=00.o00
0.0000=0000000
0.000000000000
0.0000+3.0000
0,00000=46.00
0.0000++0.000
L3-FTTCCRIPPR) Omies
0.4.74
0.4167=000200
0,4267 s0.020s
0.2718
1. 1.0000 a. 0000
0. 19667700087
L3-FTICRISPR) Omiics KG
0.4/50=00.007
0. 1600600000065
0.9006=33.470
0.95000+000000
G-Retriever + pre-GAT
0.4:1300.0000
0.3000=000000
O. 0.1021=00000
11251822.000
1. 1.0000000oo 0000
0.9500000..ooo 0000
RoG
0.4718
0.4400
0. a0.529
0. so.004
0,9 0,9667
0.
SubgraphRAG
0,16:1==0.012
0.1233=0.00a5
0443
0.2833=23.006
1 1.0000=0.00m 0.0000
9333-+000299
GNN-RAG RAG
[.4855gu.coos
g.4455
0.2880
0.9000)ac.wo
0. 0.9000 10000
GALAX
01.442200.c22
0.4283=0.00am
0. a.0
so.017
a.9533
0. 1.9667 0.9667=u.02m
GALAX (Qwea2.5-7B)
1.4787=0.0085
0.4667 70 0188
0.4722:0.00 0070
0.33550000004 0004
1 1..00000..0om 0.0000
0.9000)++0..000
Table 28: Model performance on LUSC
Model
Precision
Recall
FI 1
Jaccard
Hive 1
Hit@ 10
MET
0,0000
0,01000
0,0000
0,0000
0,03000
0,0000
GAT
0.000020.co00
0.0000=000000
0.0000000000
0.0030g3.000 0000
0.0000ac 0000
0.0000 0000
L,3 Omies
0.0222=0.c19
0.0067=0000s
0.010820
0.0052a2.0045
0.0000 0000
0.0000 0000
L3 Omics KG
0.0162=00.oss
0.00150=0.000
0.0055e0
0.0028-2..022
0,0001)
0.90000++0.000
L3-FT(Med) + Omnics
0.016620.0106
0.0083 000s
0.008G=00000
0.0048=2..000 0005
0.00000ee 0003
0.9000 0000
L3-FT(Med] Omies KG
0.0099L=p.ce2
0.00833=00002
0.00192000045
0.00255::.021
0.00010
D. 0.00000)+0.0000
L33FTTCRRIPRR- Omies
0.5208 co.c
0.4767-000810
0.1961 0607
0,3320=3300 0515
0,9007 6557
0.9167++0.00 6577
L3-FTICRISPR) Omies KG
u.4666:u.cass
0.4217=0.0te
0.442Ds0.047
0.28:1:0..uss
0,93533es. 1283
0. 0.9833
G-Rotriever pre-GAT
0.4563=000679
0.3
0.59
0.24:33e0.011
1.0000=0.0om
0.99000:+s.0000
RoG
0.5038=0.224
0.1350=00.0004
0
0.3128=23.0ss 0.583
1 1.000000.00om 0000
O. 019666==0 =77
SubgrapARAG
0.5360=0.0m2 0133
0.4550=0.04s
0. 1868
0..287
1. 10000=0.0000
1 1.0000
GNN-RAG
0.1851=9.ca07
0.4717=000575
0.1782e00519
0.3161e2.0208
1 1.0000=..oom 00000
0. 183336000077
GALAX
0.5016=n.c0ss
0.4783=00.00s
0. 0915
0.3286=22.072
1 100000000000 0000
D. 0.883349.022 0250
GALAX (Qwen2.5-7B)
a0.212
0.5150:0.01a0
0.5193:0.0m 0190
0.3509::0007M
0.9333ac.0577
Table 29: Model performance STAD
Model
Precision f
Recall f
F
Jaccard f
Hir@5 1
Hire 10
M2T
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000_
0.0000100000
1.0000.00.000
0.0000_00000
0.00001c.000
0.0000 L0.0000
L33 Omics
0.0000__.ew0n
100000
0.0000.10.000
0.0000
0.00001s.0om
0.0000__..000
L3 Omics KG
0.00000=0.0000
0.00000a00000
0.00001=0.0000
0.0000=0.000
0.0000)acc.0m 0000
0.00100=000000
L3-FT(Mcd)- + Omics
0.0057=000100
0.0017+a.0029
0.0023++0.000 0044
0.0013=00.028
D. 0.0000 0003
0.0167=0.0250
L.3-FT(Med) Omies KG
0.0000=0..o00
00000
0.0000.0.00
0.0000=0..o0m
0.0000aa 0003
0.0000
L3-FTCCRISPR)+ Omics
0.5528=000511
0.5200e00040
0.5353+0.0425
0 1=000009
1.93333e..00 0537
0.9333=0.0259
1.3-FTCCRISPR) Omics KG
0.5306=0.ca1
0.5000s00422
0.5144a0.00 .392
0.34
D. 0.90000a6,.0 0003
0.8667
G-Rerriveer pre-GAT
0.6374=0.0011
0.5193 0084
0.3550000.011
0.9000sso 0003
0.9000 .000
RoG
0.5099
0. 0.40501
0.5020 1o 10.6
0.3382 _ 0.0457
0.8000 2545
0.7167 27167_0..002
SubgraphRAG
0.50444:0.448
0. a
0.4993
1.7667....200
0.7667=v0.211
GNN-RAG
0.5387=0.0068
0.5267
0.53225=0.007
0. 0.3680=a0.oor
1.
1.0000=0.0000
0.8167=000077
GALAX
0.5298=0..881
0. 10108300004
0.5131=0.0371
0.3
0.9667=2.0557
0. 0.86167=0.0250
GALAX (Qwen2.5.7B)
0.5731:0x1112
0.5883.10 0029
0. 0.5803:u.u0s
0 0.4117 00.07
1.0000 0000
0. 1.98331..02284
Table 30: Model performance on MB
Model
Precision
Recall
F1
Jaccard
Hic0
Hiu@e 10
M2T
0. 0.0000
0,0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000_0.000
0.0000_oo00
0.0000
0.00001..000
0.0000....0o
0.000010.000 0000
1.3 Omics
0.0333
0.0067_oon
0.0111100102
1.0058
0.0000.c..000
0.8000
1,3 Omies KG
0.0126f20.cmt
0.0067=00115
0.008720016)
0.004422.000 0076
0.0000/a0 0000
0.0000+a0.co 0000
I.3.PT(Med) + Omics
0.01 10.
0.0100_0o000
0.010210.0ee
0.0052
0.0000 1: 0000
0.0000 00000000
L3-FT(Med Omics KG
0.0282=0.cea7
0.0167=0.000s
0.0192+00000
0.0097 0008
0.0067
0.0333+400077
1.3-TTCCRISPR) + Omics
0.5308 c0.07
0.5164200040
0.348810 am14
0.8087.5 1155
0.8000 1000
L3-FTICRISPR)+ Onnics KG
0.5086G20.ces
0.5000==0.000
0.5013200000
0.3357=330000
0,7333
0. 0,7000
G-Retriever + pre-GAT
0. 0.35-16i=0.0d 0421
0.3500=00175
0.4305
0.2794e2.0215
D 0,80001ac 0000
D. 0.700004..0r 0000
RoG
0.5530g0.com
0.540010..11
0.546410.000
0.3761 110 o18a
1. 0,0000 n0on
0. 1150000....
SubgraphRAG
0.55155=0.c119
0.3900=0.2112
0.1302200787
0.2925
0.8067
0.9000++000000
GNN-RAG
0.530620.co67
0.4-67 1007
0. 0.4770500000
0.3151:2.051
0.96339e6 1155
0. 1.9667=..oom 0577
GALAX
20.
00
0. 48882.0
0.3233
0.83333..00
GALAX (Qwen2.5-7B)
0.5300=0.2440
0.5300=00s40
0. 0.5
0.3611=2.0125
0,8007#6.1.38
0. 0.933334000077
33

---

Published as a conference paper at ICLR 2026
Table 31: Model performance on ALL
Model
Precision
Recall t
F
Jaccard 1
5 1
Hit@l 10
M2T
0.0082
0,0050
0.0002
0.0031
0,0000
0,0500
GAT
0.000020.c00
0.0000_..0000
0.0000000000
0.00001a..000
0.0000 0003
0.0000 0000
1.3 Omics
0.0333L0.0077
0.0033
0.0061 Looter
0,003110.00
0.0686 1153
0.0339310.0077
L33 + Omics KG
0.0000=0.c000
0.00000-0 0000
0.00000000000
0.0030:3.000 0000
0,0000) 0000
0.0000+a..00 0000
1.3.PT(Med) + Omics
0.001620.com
0.0033_00000
0.0021 10.000
0.001023.001
0.0000 0000
0.0000 0000
L3-FT(Med Omics
0.0607=0.062s
0.0133=00015
0.0219:00188
0.0111=2.0006
0,00000::c.
0.0000+++0.000
L.3-PTYCRISPR) - Omies
0.5447200.com
0.5-400.00.0044
0.5443200002
0.375423.050
1 1,00002 0000
0.9333 1155
L3-FTICRISPR)+ Omics KG
0.6010=p.cos
0.5800=0.1100
0.5786=0.000
0.110623.0572
0,8333e0,
D. 1.955660.116
G-Retriever + pre-GAT
0.6285=00.04s
0.5300=000000
0.5750=0
0:366222.0011
0,80000 0000
D. 0.80000)=a..o 0000
RoG
0.5096g0..oo
0.5133=0 1901
0.6326ss0 1o0
0.3684 1002
0.9333as 11:5
D. 0..6677 00007-40.007
SubgraphRAG
1.6000=0..c00
0.6000000.000
0.0
0.1286=2.0000
1 1.000= 0000
1.0000=00000
GNN-RAG
0.5682200.css
0.5200.0.1oo 1000
0. 0.5402e0 1203
0.3770 1186
0.9333ex 1165
0. 1.906670.0077
GALAX
D. 6866000
0.6700s0
0.677
0.5181
1.0000:0000 0000
1.933910.000
GALAX (Qwea2.5-7B)
(1. 0.6000==0..02
0.62000=0.002
0. 0.6000000.0020
0.1299=2.0552
1 1.000=0.0000
1 1.00000000000
Table 32: Model performance on LGG
Model
Precision
Rocall
F
Jaccard 1
5
Hit@ 10
M2T
0.0000
0,0000
0,0000
0,0000
0,0300
0,0000
GAT
0.00000
0.00000=0000p
0.00000ga..0o
0.00I0ea..ooo
0.0000) (000
0.0000)as..oom 20000
L3 + Omics
0.00000=n.com
0.00000=0.000
0.00000sa.0000
0.000Dea..ooe
0.00000=s..000
D. 0.8000)+.,.000
L3 Omics KG
0.0063=00.om2
0.00333000005
0.0011+00000
0.0021+2.000
0.0000) 0000
0.90000=00 0000
L3-FTIMedd + Omicss
0.01333
0000022000000
0,0046
0.00010
0,0000 20.00000
L3-FT(Med) Omics
0.0000==0.000
0.0000000:0000
0.00
0.00300=2.000
0,00000:+6.0000 0000
0.9000+++00000
L3-FTTCCRISPR) Omies
0.5000 10.00
1.4367_.00mm
0.4649gou54e
0.304010.0057
1.0000 2000000
0.9667 11066710.0177
L3-FTICRISPR)+ Omnes KG
=0.c25
0.5067:=0.115
0.5163200143
0.9450s2.0125
1 10000=0.0000
0. 1,0000
G-Retriever + pre-GAT
0.48 16-006077
0.45/Dea.0125
022037
1. 1.00000.0000
0.900I)+o.0co0 00000
RoG
0.5667:1.0460
0.5667 10 0462
0.5667
00.042
0. 1.3963 10.04s
1.0000 10000_0.000 oonn
0.9667 10.0577
SubgraphRAG
(1.
0.5:000=0.0000
0.5
0.3699-e0.0000
1.0000=0.0000
0.0000000000
GNN RAG
0.2700_ _7700__.00000
0.35761000000
0.2177
1.8000
0.500016.0000
GALAX
0..433.0..2552
0.5433
L.
0.3733
1.0000
0. 10.000
GALAX (Qwen2.5-7B)
1.5501=9.cop
5..00000..00
0.8687
0.5667
Table 33: Model performance on NB
Model
Precision
Recall
F1
Jaccard
Hicel
Hiu@ 10
M2T
0.0000
0:0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000_n.cw0
1.0000_
0.0000
0.00001a.0000
0.0000
0.000010.000
1.3 Omics
0.0000g..com
0.0000-0
0.000000000000
0.0000
1.0000
10.000
Omics KG
0.030020.co0m
0.0000_000000
0.00001000000
0.000023 0000
0.0000 0000
0.0000.... 0000
L3-PT(Med)) + Omnics
0.0085_000144
0.0167_
1.0113100190
0.0057 10.0100
0.0000 1:0 1000000
0.000011..000
1.3-TT(Med) Omics KG
0.000020.c000
0.0000-000000
0.0000
0.000012.0000
0.00000:30.00
0. 0.0000
1.3-TTCCRISPR) + Omics
0.5587 L0.047
0.5253__0.00
0.5407 Lo
1.3718
0.9667 100.2000
0.8667 1.. 1524
L33FTTCCRISRR+ Onnics KG
0.5507 20.000
0.5067=00022
0.5276200000
0.3603523.000
0,9333ac 1163
D. 0.8007490.077
G-Retriever pre-GAT
0.4322:0.0444
0.30
0.41192000s
0.2509 0361
1. 00000:0 0000
0. 0.9333_. 0877
RoG
0.5997.1o
0.4133_
0.481710000
0.317910.0233
1 1.0000 o 0000
10.1155
SubgraphRAG
0.5679=0.c451
0.4667=0000m
0,50H8200w0
0.3383=20001
1. 1.000000000
9333-00.158
GNN-RAG
0.581020.cws
0.673310 oza1
0.5771 oo 0198
0. 10.
0.733316 2009
0.73391...0r 0577
GALAX
0.55331000115
0.3533_00115
1.0533
0,382510.0mo
0.8667.1
D. 0.8667.10.
GALAX (Qwen2.5-7B)
0.5733=0.0442
0.5733+0 048g
1.6733800040
O 0.402863.046 0406
0,8000acc 0000
D. 1.0070000077
Table 34: Model performance on MESO
Model
Precision
Recall f
F
Jaccard 1
Hir@ ?
Hit@r 10
M2T
0. 0.0070
0,0050
0.00G8
0.0029
0.0000
0.0000
GAT
0.0000=a..ooo
0.0000==00000
0.00000=0.000
0.00I0ee..000
0.000 0000
0.900 0000
L3 + Omics
0.0000==n.coo
0.00000=00000
0.00000000.000
0.0000e2..000
0.00000 0000
D. 0.9000):+0.0om 0000
1,3 Omies KG
0.0000=0.co00
000000000000
0.0000000 0000
0.000023.000 0000
0.0000 0000
0.0000:+...0 00000
L3-FTiMed) + Omics
(10190
0.0167=00020
0.0153:00123
0.0067
0.00000 0000
0 0.00001 00000
L3-FT(Mesd) Omics KG
0.0192=20co2s
0.0100=0.0000
0.0104:00002
0.0053=3.000 0006
0.00000:40.00
0.00000+0..0
L3-FTTCRRSPRR Omies
(.5152=0..w00
0.4833=000874
0. a0
0.33511=0.0720
0,9333asc 1155
0.5667=+0.1122
L3-FTTCRISPR)+ Omiic KG
0.5233=000416
0.5322e00402
0.3633=22.0994
O. 0.90000+0.0000
G-Retriever + pre-GAT
0.4721 20.072
0.3733=0 0493
0.2030 0265
1. 0,00000000m 0000
D. 0,800004..00 0000
RoG
d.1476=u.cra
0.39
1.4197=0.1188
0,8667
D. 0.5000l+s0.000
SubgraphRAG GNN-RAG
0.1121 =0.0000
0.1200=000000
0:0000
0.27 1.27765=2.0000
0. 0.60010:ec.000
0.5000++0.000
12:0000000000
0,2927 o00000
0.1714:s.une
1.0000 1.:000....00 000000
0. 0.5000l:a..ooo
GALAX
0.5933 0231
0.5933 0231
0.5933:
0.4221::0.o2n
1 0..000=0.0000
0. 0.50000++..00001
GALAX (Qwen2.5-7B)
0.5621=0.0s10
0.5583=000402
0 055576
0.3872=0.0307
1. 1000000.0000
0. 0.99333000000 00007
34

---

Published as a conference paper at ICLR 2026
Table 35: Model performance on LIHC
Model
Precision
Recall f
F
Jaccard 1
5 T
Hit@l 10
M2T
0,0000
0,0000
0.0000
0,0000
0,0000
0,0000
GAT
0.010020.0000
0.0100_.00000
0.0100200000
0.0050ga..00 0009
0.0000 0000
0.000010.000 0000
1.3 Omics
0.0000_0.comm
0.0000_0.0000
1.0000
0.000010.0000
1.0000
0.000010.0000
L33 + Omics KG
0.0300=0.c000
0.00000.0 0000
0.0000000000 0000
0.0000=2.000 0000
0,0000):30 0000
0.0000+a0.00 0000
1.3.PT(Med) + Omics
0.0031 c0.07
0.0067
0.0043100007
0,0021 100001
0.0000 0000
0.9000
L3-FT(Med)) Omics
0.0000=0.c000
0.0000=00.000
0.000000000000
0.0000=3.0000
00000
0.00000:=4
0.0000++0.000
1.3-PTYCRISPR) - Omies
0.4239
0.30672.0.051
O. 000
0.7333ka 1155
0.7000 1730
L3-FTICRISPR)+ Omics KG
0.4552=0.co6s
0.4267=00.008
0. 1500000000000
0,292 te3.0512
0,80000
0. +0700T+9.0677
G-Retriever + pre-GAT
0:30000=0:0020 0.3508=00.ce7
0.3
0.1961=2.0500
D 0,8000) 0000
D. 0.7007++0.. 1165
RoG
0.4206
0.3733=0 1856
0.3003 1396
0,2500 1207
0.9333as
D. 0.800040..170 1790
SubgraphRAG
0.1738=0.:824
0.1267=0
1845
0.2961
0,93333-60.1
0.8000++0.1795
GNN-RAG
0.4233200.089
0.42233=b..0a0
0. 0283
0,2088:22.2290 0290
1 1000:00000m 0000
0. 8.33333000077 0177
GALAX
0.490020..0
0,4300
0.4900:0
0.3245.0000m
1,0000=0
9000000000000
GALAX (Qwea2.5-7B)
0.1362=0.0os
0. 0/43333000000
0,13 18000000
0,2778e2.ess
0,8087#6.1388
0. 0.5667700.0777
Table 36: Model performance on LAMI.
Model
Precision
Rocall
FL
Jaccard 1
5
Hit@ 10
M2T
0.0161
0,0100
0.0121
0.0062
0,0300
0,0000
GAT
0.0000==m.com
0.00000=00000
0.00000ga..0o
0.00I0ea.ooo
0.0000) (000
0.0000) 00000
L3 + Omics
d.0.000=n.com
0.00100=0.00m
0.00000aa.0000
0.000Des..ooe
0.00000=s..000
0. 0.8000)+0,0000
L3 Omics KG
0.1576==0.44
0.0200=00175
0.0351=00007
0.0152=2.0166
0.1333++0 2309
0.20000=+..122 1782
L3-FTIMedd + Omicss
11.06
0.04000:0.11rs
0,0496
0.02542:0.0044
0,20010) 5464
0.1333 0.13333a0.112 20.3300
L3-FT(Med) Omics
0.1085=00.007
0.0267=0.000
0.0
0.0193-23.014
0,20010)+00
0.20000++0.177
L3-FTTCCRISPR) Omiis
114758_..ccm
0.37333_.0115
0.4180ao.0ea
0.264222.moos cous
0.8667 1.6677c..220 2000
0.9000 201M
L3-FTICRISPR)+ Omnes KG
0. 0.4988#0.om
0.4500==0..000
0.4721:0.00
0 0.8102:000481
0,8687#002109
D. 0.8553800.118
G-Retriever + pre-GAT
0.3719:s0.cts
0000000000000
0,2912e00119
0.1706:00.0081
1. 1.0000=0.000 0.0000000000
1.000000...co0
RoG
0.4228 10.014
0.3466 1011
0.37 10
0.2303
1.0000 a0 oonn
0.8667 0.5667.19.115 10.1155
SubgraphRAG
0.4144
0.4100==0.000
0.2596
1. 1.0000=0.00m
0.55000+0.0000
GNN RAG
0.3972 jo.000
0.3200_
1555
0.351 10:133
0,218310.1015
1.0000 o 0.0000
0.8333310.077
GALAX
0.4730.0.0m0
0.4333
0.4510
0.2923
0.93331..1155
0.8667
GALAX (Qwen2.5-7B)
0.4506
0.4567 so 0452
0.4581
0,2979
1.0000 000000
1 1.0000
1..0000000000
Table 37: Model performance on DLBC
Model
Precision
Recall
F1 1
Jaccard
Hicel T
Hiu@e 10
M2T
0.0000
0:0000
0.0000
0.0000
0,0000
0.0000
GAT
0.0000gn.owp
1.0000_
0.00000
0.00001a.0000
0.0000
0.000010.000
1.3 Omiccs
0.0000g..com
0.0000_00000
0.000000000000
0.0000
0.0000
19.000
Omics KG
0.0300=0.co0m
0.0000_000000
0.00001000000
0.00002a.00m 0000
0.0000 0000
0.0000....0 0000
L3-PT(Med)) + Omnccs
0.0220_u.com
0.0066__00000
0.027510000
0.0141 Lou055
0.0000 1. 10000
0.0000.1..000
1.3-TT(Med) Omics KG
0.0876=2.ca85
0.0067=00000s
0.00566:2.00 0009
0.00807
0.033311..00
1.3-TTCCRISPR) + Omics
0.3985_1..omm
0.3900__0077
0.2470Lo.0500
0.9333 1155
0. 0.8033 15333.0.0577
L33FTTCCRISRR+ Onnics KG
0.4200g0.co30
0.4100=00015
0.414920.0000
0,261922.006
0,8067 1163
D. 0.7000so,.
G-Retriever pre-GAT
0.3196220.c07
0.25
0,2903200166
0.1740a3.0115
1. 1.0000:0 0000
D. 0.8000020.00 0000
RoG
0.4424 10.0om
0.4367 00.5
0.4395
0.281910.02m
0.8687719 11:3
0.7667.11.111
SubgraphRAG
0.4286=0.co00
0.4200-0.0000
0.1242200000
0.2092 0000
0,8000 0000
0.700044..00 0000
GNN-RAG
0.4403.0.cmm
0.4
0.443310
0.28501:.0m
1. 1,0000 000n
0. 0.9000_ 0000
GALAX
0.4400.0.com
0.4400
0.440010.000
0.2821 10.0000
0.8667
0.8333
GALAX (Qwen2.5-7B)
0. 0.4764410.d ooss
0. 0.4700 0000
0.4732:0.00 0027
0.3999000022 0028
0. 0,8000ac 0000
0. 1..9000..000 0.0000
Table 38: Model performance on MM
Model
Precision
Recall f
F
Jaccard 1
Hir@ ?
Hit@r 10
M2T
0.0000
0,0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000=nn.ooo
0.0000==00000
0.00000=0.0000
0.00I0ee..c00
0.000 0000
0.900 0000
L3 + Omics
0.0000==n.coo
0.00000=00000
0.000
0.0000e2..000
0.00000 0000
D. 0.9000)+00.0o 0000
1,3 Omies KG
0.0000=0.co0m
0.0000=000000
0.0000100000 0000
0.0000:3.000 0000
0.00000ac 0000
0.00000+.... 0000
L3-FTiMed) + Omics
(1.275
0.0167=00205
0.0156200148
0.00799e0..07s
0.06687 =0. 1183
0. 0.01335
L3-FT(Mesd) Omics KG
0.1091
0.0200=00000
0.03
0.0167=3.00
0,1333
0. 0.1333++..r
L3-FTTCRRSPRR Omies
0.4000 =0.002
0.3800==00205
0,3804
0.2018e0.mmm
0.9333 1155
0.900))+s0.000
L3-FTTCRISPR)+ Omiic KG
0.17538e0.oos
0.43
0.15 192004774
0.2952e220057
0,80000sc
0. 0.733346.0077
G-Retriever + pre-GAT
0.451:4=0.com
20000200000
0,40
0,2561 0500
0,9333e40 L165
0,9000000..00 0000
RoG
.,900
0.38133:01100
16
0.2745::0.usn
0,9:533les 1183
D. 56677
SubgraphRAG
0.1515=00.122
0.3133=00.220
0.3620=00175
0.2211=3.0190
0,91339e0.111
8667+00.007
GNN-RAG
mn.cmm
0.5133:0psass
0.5150
0.3474:00umto
1.0000 a.:000...mm 0000
0. 0.8333la0..m 01177
GALAX
0.5858 0564
0.5700
0.5775
0.4082
1 0.0000
0. 1.933330.0007
GALAX (Qwen2.5-7B)
0.50000=0.our
0.5000=00.00
0. 0.5
0.3387
0,9333+ 1165
0.9000+40.0 1000
35

---

Published as a conference paper at ICLR 2026
Table 39; Model performance on KIRC
Model
Precision
Recall 1
F
Jaccard 1
5 1
Hit@ 10
M2T
0,0000
0,0000
0.0000
0,0000
0,0000
0,0000
GAT
0.000020.c000
0.0000_..0000
0.000000.000
0.00001a..00
0.0000 0000
0.000010.000 0000
1.3 Omics
0.000000..omm
0.0000_o..00
1.0000
0.000010.0000
1.0000
0.000010.000
L33 + Omics KG
0.0300=00.000
1000000000000
0.00000000000
0.0030g2.000 00000
0,0000):30 0000
0.0000aa0.00 0000
1.3.PT(Med) + Omics
0.0000000.cm0
0.0000__00000
0.00000000000
0.00002..0000
0.0000 0000
0.0000 0000
L3-FT(Med) Omics
0.0000=0.c000
0.0000=000000
0.00000000000
0.0000=3.0000
0.00000:=0
0.0000++0..00
L.3-PTYCRISPR) - Omies
0.5488
0.5333_.00013
0.6394200475
0.3
0.8007 as 2309
D. 0.50G7 1155
L3-FTICRISPR)+ Omics KG
0.4897
0.4800=000206
0.1817#002255
0.3201
0,8087=0.1100
D. 90000000000000
G-Retriever + pre-GAT
0. 0.1160=0.216
0.3733=0000s
013
0.26/9e2..1s
0,60000) 0000
D. 0.800 0000
RoG
0.6207 20.017
0.5867 so 0751
0. 6031
so 0680
0 0.4340::00m 0087
1 00000=0 0000
1 1.000000.0000
SubgraphRAG
0.6412++0008
0.3300=0.1m82
0.1067=001100
1.000000.00m
9830++0.116
GNN-RAG
0.5858820.c850
0.5733
0.6812200009
4118
1 10000 0000
1 1000000.000
GALAX
0..797
0.5700
0.6748800055
0,4036 0247
0,9333
909000000
GALAX (Qwea2.5-7B)
0.5786=9.3114
0,5700=0.1044
0.57 0.5712ea.uore
0. 1990.190
0.8087#6.118
0 0.93336000077
Table 40: Model performance on THCA
Model
Precision
Rocall
F
Jaccard 1
5
Hit@ 10
M2T
0.0000
0,00000
0,0000
0,0000
0,0000
0,0000
GAT
1100000=0.com
0.00000=00000
0.00
0.00I0ea..ooo
0.0000) 1o00
0.8000) (0000
L3 + Omics
d.00000=n.com
0.00100=0.00ma
0.00000sa.0000
0.000Dess.ooo
0.00000=s..000
O. 0.8000)+9,0000
L3 Omics KG
11500=0..100
0.0200=00175
0.0318400000
0.017990.0106
0.1333 2309
0. 0.10000++.112 1782
L3-FTIMed) + Omicss
0.0167 00:167=00000
0.02110:00111
0.0101 souao0s
0.0667 20.7770.100
0 0.013333
L3-FT(Med) Omics
0.0156=0.0146
0.0300-000205
0.02116e00117
0.0101=2.0005
0,0001):+0
0.01333+000077
L3-PTTCCRISPR) Omics
1.54685mmccm
0.5387_.0003
0.5430g
0.3728
0.8687 1:55
0.8333119.1077
L3-FTICRISPR)+ Omnes KG
0.5818=n.c40
0.5313=000415
0.535620.0005
0.9850e00.006
0,8333e0.1188
D. 100000
G-Retriever + pre-GAT
0.6237=000540
0.3833-000221
0.4718a000077
0.3088
0,5333 1:55
0.7533++0.115
RoG
0.5806_u.cc00
0.5366 00.05
0.5524 100009
0.3827 Loueess
1. 1.0000 Lo oonn
1.0000
SubgraphRAG
0.5:18-00.000
0.50000=000000
0.5
0.3546ee2.0000
1.0000
1.0000-0.0000
GNN RAG
0.64651o.omm 10.00
0.4400_ 12.10
10.127
0.347310o.77m
0.580316.1155
0.533310.0577
GALAX
0.607610.c0m
0.603310.cm
0.605_.o
0.4346.0..0
1 1.0000
1.0000
GALAX (Qwen2.5-7B)
0.5750=0.c30
0. 7.4967-00773
0.5312e0.056
0.3622
0,80010
2000
0.5333+0.0077
Table 41: Model performance on BLCA
Model
Precision
Recall
F1 1
Jaccard
Hicel
Hiu@ 10
M2T
0.0000
0:0000
0.0000
0.0000
0,0000
0.0000
GAT
0.0000_n.cw0
0.0000_..omm
0.0000
0.00001a.0000
0.0000
0.000010.000
1.3 Omics
0.00000
0.0000_0.000
0.000000000000
0.0000
0.00000
19.000
Omics KG
0.000020.c000
0.0000_000000
0.0000100000
0.00001a 0000
0.0000 0000
0.0000.10.0 0000
L3-PT(Med)) + Omncss
0.0127 1o0111
0.0267__0022
0.0172
0.0087 Lou0ors
0,0000 1.0 10000
0.0000
1.3-TT(Med) Omics KG
0.0222:2.cas5
0.0033-000005
0.0008200100
0.0020:0.00 0051
0.00000:30.
0.033311..00
1.3-TTCCRISPR) + Omics
0.553810.cop
0.5000_o.0000
0.5250Loo0m
0.356310.0rm
0.73333 1155
0.5000 0000
L33FTTCCRISRR+ Onnics KG
0.561720.coe4
0.5307=000031
0.5484220056
0.3784222.066
0,8067
0.800T
G-Retriever pre-GAT
0.5025=20.ce8
0.4733=00000s
0.4870220855 0853
0.3221 o23
0.8000ss. 0000
0.633332..o 0577
RoG
0.563310.com
0.5633 0z31
0.5633
0.39241o.0zz
1 1,0000 0000
0. 1.9667 _0.077
SubgraphRAG
0.5467=20.o07
0.4633=0 1501
0.4922e0 (00)
0.3302 os5a
0,9333ea0
0.9000+0.
GNN-RAG
0.5270go
0.4900_.0.1o 1616
0.505611 1076
0.3450-..117 1177
0.8067.1..5774
0.8000 51.5
GALAX
0.524720.c0m
0.5033
0.0133
0.3453
1. 1,0000
D. 1.90001...100
GALAX (Qwen2.5-7B)
0. 0.58522#.00 0091
0.5733+0.00 0118
0.5791 0018
0.4006:0.0018
0,9833a6. 1163
0.90000+0.00 0000
Table 42: Model performance on UCEC
Model
Precision
Recall f
F
Jaccard t
Hir@ ?
Hit@r 10
M2T
0.0000
0,0000
0.0000
0.0000
0.0000
0.0000
GAT
0.0000=n..ooo
0.0000==00000
0.00000=0.000
0.00IDee..c00
0.000 0000
0.900 0000
L3 + Omics
0.0000==n.coo
0.00000000000
0.000
0.0000e2..000
0.00000 0000
D. 0.90000+s0.ooo 0000
1,3 Omies KG
0.000020.co0
0.0000-00000 0000
0.0000000 0000
0.0000 0000
0.0000aac 0000
0.00000+... 0000
L3-FTiMed) + Omics
100000==..com
0.00
0.001.0=a..000
0.00000ea..000
0.00000 0000
0 0.01001=+0.0000
L3-FT(Medd) Omics KG
0.0000=0.co00
0.0000=00000
0.0000000000
0,0000=3.000
0.00000:c0.00
0.00000+0..0
L3-FTTCCRIPPR Omies
0. 0.5293+0.oom 0004
0.5183==0a570
0.521
0.3530ee.0054
0.7333la 1155
0.700ll+o, 1000
L3-FTTCRISPR)+ Omiic KG
0.1672=0.0472
024
0. 05400
0.2991
0,80000e0
O. 0,8000+40, 1000
G-Retriever + pre-GAT
0.4517 150171
0.1000=00175
0. 0277
0.200
1. 0.00000000o 0000
0. 9000000000m 0000
RoG
0.450S8 cu.coos
0. 004
0. 0.1356
0.28013:0.0014
0.6 0.6667
D. 16.66670..000 #041
SubgraphRAG
0.1500=00co00
0.1800=000000
0000
0.31558=2.0000
1. 10000=00000 0000
0.7000++0.oco
GNN-RAG
0.44333:0.1100 1159
0. 1.4589
0.3011 souuros
0.6000) n212
0.4667 4144
GALAX
(15280 =n.cos
0.5233
0.5266::ooo
0.3579
a000000
0. 9.00000.0000
GALAX (Qwen2.5-7B)
0.50333=00051
0. 051
0.3282=3.0286
0,9333+4 1365
43333+0.116.
36

---

Published as a conference paper at ICLR 2026
Table 43: Model performance on PRAD
Model
Precision
Jaccard
5
MZT
0,0000
0,0000
0.0000
0.0000
0,0000
0.0000
GAT
0.0000=n.cmm
0.0000000000
0.00000aa0000
0.0000sa.ee0
0.0000
1.0000
1. Omics
[.01l]sncus
0.00:33-umms
0.0026aauuos
0.0000) mo00
0.900022..00
L3 + Omics KG
0.00000=0.c00
0.000000000000
0.000000000000
0.0010e2..000
0.00000=c.
D. 0.00000)+0.00000
L3-PT(Med) Omnccs
0.0145
0.0054
2000077
0.00000
L3-FT(Med) + Omics + KG
(.0176=n.c21
0.0067=0.000
0.007850.000
0,00010:sc.om (000
0.01333+000017
(TCCSSRR))) Ornics
0.6021
0.5800_
0.5796100w
0.4088
1,0000
1..3333
L3-FTICRISPR)+ Omies KG
0.6033200.c111
00115
a00002
0,9333
0.9000)ao. 1010
G-Retriever pre-GAT
0.474020.com
0.4433_0080
0.458420014)
0.20742200000
1. 1.0000 0000
1.0000_.0000
0.5833_oo000
L0.0537
SubgraphRAG GNN RAG
0.5831 100.
0.4500=000001
0.5042200000
0.3389223.007
0.8067
0.633330..0m 5008
0.6140_0.01as
0.6100_00175
0.6120100156
0.441110mmx
1.0000_0.0mm
1.0000_o
GALAX
0.5155 0.5152_n.:000
0.47733_oo0m
0.492310.100 1006
1.9539
199667.0.0077
GALAX (Qwen2,5-7B)
0.6195:0
0.6133s0.00 o280
0.6164::0.omm 0200
0.4459=:00000
1.00000000000 0000
0.500005.0000
37


Reviews:
[{"review_id": "TuSpEWDV7m", "reviewer": "Reviewer_mMpP", "summary": "The manuscript introduces GALAX (Graph Augmented LAnguage model with eXplainability), a framework that integrates pretrained Graph Neural Networks (GNNs) with Large Language Models (LLMs) through reinforcement learning guided by a Graph Process Reward Model (GPRM). The authors also introduce Target-QA, a benchmark combining CRISPR-validated targets, multi-omic features, and biomedical knowledge graphs across cancer cell lines, supporting both GNN pretraining and long-context text-numeric graph reasoning. The work targets explainable, reinforcement-guided subgraph reasoning for interpretable pathway and target discovery in precision oncology.", "questions": "see above", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.  Bridging numerical multi-omics, graph structure, and textual knowledge for mechanistic interpretability in precision medicine.\n\n2.  Target-QA dataset/benchmark combining CRISPR screens, multi-omics, and biomedical KG, enabling both supervision and evaluation.", "weaknesses": "- Need stronger baselines (e.g., state-of-the-art KGQA, graph-augmented LLMs)\n\n- It’s unclear how well GALAX transfers across cell lines, cancer types, and unseen targets, especially under shifts in omic distributions or KG incompleteness.\n\n- Human-in-the-loop or expert-curated assessments of subgraph plausibility are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2Ec45UQD6m", "reviewer": "Reviewer_4LvX", "summary": "This paper tackles target discovery in precision medicine by integrating multi-omic features, biomedical graphs, and literature via GALAX, which couples an LLM with a pretrained GNN and a Graph Process Reward Model to build explainable, RL-guided subgraphs. On the new Target-QA benchmark, GALAX consistently outperforms strong language-only and graph-augmented baselines on precision/recall and Hit@5/10, with qualitative pathway enrichment analyses supporting biological plausibility.", "questions": "- There are minor typos: two consecutive “and” on p.2, line 64, and duplicated “has” on p.4, line 186.\n\n- The symbol 𝜀DTI appears to be used inconsistently: in the formulation it denotes disease–protein interactions, while 4.1 uses it for drug–target interactions. Please unify the definition throughout.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper introduces a clear process-level supervision scheme (GPRM) that links subgraph construction to a graph foundation model, yielding interpretable rationales rather than only outcome metrics.\n\n- The Target-QA benchmark and comprehensive comparisons/ablations (including complexity analysis and enrichment studies) make the evaluation credible and reproducible.", "weaknesses": "- Please streamline the problem formulation: many set-indexed variables (e.g., X1, X2, X3,...) could be replaced with compact matrix/tensor notation, and a schematic showing each modality and how it connects to the graph would improve readability.\n\n- The framework depends on multiple data sources (multi-omics, biomedical graph, literature); it would be helpful to analyze performance in low-resource scenarios (e.g., patients lacking certain omic assays) to gauge robustness", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8EcdKmQN65", "reviewer": "Reviewer_xSCJ", "summary": "This paper introduces GALAX, a pipeline for QA in precision medicine using a joint LLM-GNN model using subgraphs generated via a graph process reward model as explainable artifacts for reasoning. The authors also introduce a new benchmark, Target-QA, focused on biomedical graph knowledge, multi-omic profiles, and CRISPR targets.", "questions": "- How robust is the GPRM to shifts in important graph characteristics such as graph density or KG incompleteness?\n- How sensitive is performance w.r.t. the backbone LLM?\n- What are the failure modes of GALAX?  Are there particular paths/subgraphs that make any systematic failures evident?\n- How robust is the NER function? It seems this is critical for seeding a good graph.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The interpretability aspect of the method is strong and well-reasoned. Penalizing schema violations in the reward function and rewarding path-like structures is good.\n- Experiments show GALAX consistently outperforms baselines. The performance is very good.\n- Target-QA is an important contribution to the community for working with multi-omic features.", "weaknesses": "- There doesn't seem to be studies on the performance of the method when the KG is incomplete or noisy/not well aligned, which is almost always the case in real-world KGs.\n- Most evaluation is done on Target-QA. Validation on external datasets is not emphasized, making it more difficult to assess the impact of this work.\n- The authors mention reward hacking as a problem for previous PRMs. However, how the method combats this is not clear beyond the schema relation penalty. There is no analysis on how gameable the proposed graph PRM is.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZMJh9V1DGN", "reviewer": "Reviewer_KmG9", "summary": "Precision medicine relies on the use of multi-omic features, structural information as context and textual biological knowledge. Each of these aspects however, have their own shortcomings. The paper suggests leveraging their strengths by proposing a unified framework of subgraph reasoning. The paper proposes GALAX, a method that combines pretrained language models with Graph Neural Networks (GNNs) for reasoning using reinforcement learning. A process reward model over graphs guides the subgraph generation process pertaining to disease information. The final reasoning traces then leverage subgraphs to generate query responses. Authors further propose Target-QA, a QA benchmark for precision medicine, developed using the DepMap for evaluating GALAX. Empirical evaluations demonstrate that GALAX improves over considered baselines and its components are found to be performant.", "questions": "Refer to weaknesses", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The paper is combined an innovative set of ideas.\n* The paper is well organized and comprehensive.", "weaknesses": "* **Motivation:** The central motivation and idea behind the paper remains unclear. The paper states that it aims to integrate multi-omics, topological structure and textual information in order to curb their limitations. However, it remains unclear how the paper carries this out as combining these aspects leads to combining both their strengths and weaknesses. The paper does not mention on how multi-omics and structure interact with each other and how language plays an important role in representing topological structure. Furthermore, authors claim that the combination of these aspects makes reasoning explainable/interpretable. However, this claim is not empirically studied or validated as the paper does not mechanistically study interpretability of the reasoning process. It would be worthwhile to refine the central motivation and scope of the work.\n\n* **Dataset:** Authors curate and propose the Target-QA dataset. However, it remains unclear as to why the dataset is needed and how it serves evaluation. Does the dataset balance between quality and diversity of medicinal queries? Why curate a dataset from an established dataset for evaluation? Were other QA datasets in phenomics, transcriptomics and biological summarization considered? It also remains unclear on how effective the dataset is evaluating GALAX. The paper states that Target-QA consists of 363 QA pairs which are significantly low for a textual QA dataset. \n\n* **Experiments & Ablations:** My main concern is that the paper does not validate its central contribution. Experiments currently compare GALAX to different settings of data aggregation and training. However, the main subgraph generation scheme using process reward model is not empirically studied. How does RL help in the generation process? How do process rewards contribute to guidance? How does RL compare to SFT? What happens if we ablate dense rewards with sparse rewards? How does the model behave when we replace subgraphs with the full knowledge graph? These questions remain unexplored corresponding to the central theme of the work.\n\n* **Baselines:** Experimental setting currently considers different training and dataset modality regimes. However, these baselines do not put the method and its central contribution of subgraphs in perspective. It would be worthwhile to consider LLM with KGs for process rewards. Furthermore, what happens if we assess generation across different cell lines or heldout structures? Baseline ablations could help answer these questions.\n\n* **Presentation:** Overall the presentation of the paper is dense. The paper presents complex terminology which is often overloaded and makes the text harder to understand. Section 4 is presented in a vague form. Various details on the graph generation process and training could be placed in the appendix for improving readability.\n\n#### Minors\n* line 185: has has $\\rightarrow$ has\n* Table 1: what is LUAD and BRCA?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)