CDBridge: A Cross-omics Post-training Bridge Strategy for Context-aware Biological Modeling
Authors: Chang Yu
Siyuan Li
Zicheng Liu
Jingbo Zhou
Xianglong Guo
Kai Yu
Yuqing Zhou
Ken Li
Zelin Zang
Zhen Lei
Stan Z. Li
Venue: ICLR 2026 Poster (2026)
Keywords: AI4S
Cross-omics
Central Dogma modeling
Foundation models
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Linking genomic DNA to quantitative, context-specific expression remains a central challenge in computational biology. Current foundation models capture either tissue context or sequence features, but not both. Cross-omics systems, in turn, often overlook critical mechanisms such as alternative splicing and isoform reuse. We present CDBridge, a post-training strategy that unifies pretrained DNA and protein models into a context-aware framework without full retraining. CDBridge operates in two stages: (a) Seq-context learning, where a splicing-inspired token merge compresses long genomic regions into isoform-aware representations, and (b) Env-context learning, where a conditional decoder injects tissue embeddings to model expression under diverse biological contexts. To benchmark this setting, we introduce GTEx-Benchmark, derived from GTEx and Ensembl, which requires models to capture long-range exon dependencies, resolve isoform reuse, and predict tissue-specific expression levels. Across qualitative and quantitative tasks, CDBridge consistently outperforms prior methods that ignore central dogma constraints or context dependence, offering a scalable and biologically faithful solution for DNA-to-expression modeling.

Published as a conference paper at ICLR 2026
CDBRIDGE: A CROSS-OMICS POST-TRAINING BRIDGE STRATEGY FOR CONTEXT-AWARE BIOLOGICAL MODELING
Chang Yu'; Siyuan Lit.2*, Zicheng Liu", Jingho Zhou Xianglong Guo Kai Yu', Yuging Zhou', Ken Li Zelin Zang', Zhen Lei5.6,71 Stan Z. Li
Westlake University *Zhejiang University
BBeijing University of Aeronautics and Astronautics
*Nanjing University 'MAIS, Institute of Automation, Chinese Academy of Sciences "chooo of Artificial Intelligence, University of Chinese Academy of Sciences "CAIR, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences {yuchang, Stan. ZQ Li @we lake. edu, en; zhen lei@iaa ac. en
ABSTRACT
Linking genomic DNA to quantitative, context-specific expression remains cen- tral challenge in computational biology. Current foundation models capture either tissue context or sequence features, but not both. Cross-omics systems, in turn, often overlook critical mechanisms such as alternative splicing and isoform reuse. We present CDBridge, protein models into context-aware framework without full retraining. CDBridge operates in two stages: (a) Seq-context learning, where a splicing-inspinn token merge compresses long genomic regions into isoform- representations, and (b) Env-context learning. where a conditional decoder injects tissue embeddings model expression under diverse biological contexts. To benchmark this set- ting, we introduce GTEE-Benchharkk, derived from GTEx and Ensembl, which requires models to capture long-range exon dependencies, resolve isoform reuse, and predict tissue-specific expression levels. Across qualitative and quantitative tasks, CDBridge consistently outperforms prior methods that ignore central dogma constraints or context dependence, offering scalable and biologically faithful solution for DNA-to-expression modeling.
strategy that unifies pretrained DNA and
INTRODUCTION
Understanding how genomic DNA sequences give rise to context-specific expression remains central challenge computational biology. Here, context involves two complementary aspects: (1) Sequence context, where non-coding regions regulate expression and splicing, allowing single gene to produce multiple isoforms; and (2) Environmental context, where tissue type or external conditions drastically alter expression levels, even for identical DNA sequences. Accurate modeling of these processes has broad applications, including disease mechanism discovery (Kahles et al., 2018; Nikom & Zheng, 2023; Ueda al., 2024), drug safety profiling (Ryaboshapkina & Hammar, 2019), and DNA design for synthetic biology (Chen et al., 2025; Yang et al., 2025).
Despite rapid progress, shown in Table 1, existing methods fall short of bridging DNA to expression in truly context-aware manner. For example, single-cell foundation models, such as scGPT (Cui al., 2024), scFuundation (Hao al., 2024), capture tissue-specific representations but operate on gene IDs, ignoring the underlying DNA sequence that drives expression. In contrast, specialist models like Enformer (Avsec et al., 2021), AlphaGenome (Avsec al., 2025), and Isoformer (Garau-La Luis et al., 2024), attempt to incorporate DNA but typically operate on pre-cropped fragments or average across dynamic isoform usage, failing to capture the system's full complexity, Furthermore, while large-scale sequence foundation models (Zhou et al., 2024; Lin
"qqual Contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
Table 1: Comparison of model capabilities across input modalities and design aspects. CDBridge the only framework that supports full cross-omics alignment, tissue-aware reasoning, and expression modeling. General indicates that the model supports various tasks, while Specialist indicates the model is designed for seq2express regression tasks. /indicates support, Xindicates not supported.
Model
Venue
Type
DNA
Protein Central Dogma
Express Tissue-ware
scGPT (Cui al., 2024)
Nat. Methods
General
scFoundation (Hao et al., 2024)
Nat. Methods
General
x
x
X
GeneCompass (Yang et al., 2024)
Cell Research
General
x
DNABERT2 (Zhou al., 2024)
ICLR
General
/
X
X
NTv2 (Dalla-Torre ot al., 2023)
Nat. Msch. Insell.
General
/
X
X
X
HyenaDNA (Nguyen al., 2024b)
NeurlPS
General
Evo (Nguyen el al., 2024a)
bioXiv
General
X
Evo2 (Brixi al., 2025)
hioXiv
General
X
CD-GPT (Zhu et al., 2024)
bioXiv
General
/
X
LacaOne (He et al., 2024)
CaLM (Outeiral & Dcane, 2024)
Nat. Mach, Insell.
General
General
/
/
/
X
Enformer (Avsec al., 1021)
Methods
Specialist Specialist
/
/ /
AlphaGenome (Avsce et al., 2025)
bioXiv
/
X
Isoformer (Garau-Laii
2024)
NeurIPS
Specialist
/
5
CDBridge (Ours)
Ours
General
/
et al., 2022; Ji et al., 2021; Zhou et al., 2023; Nguyen et al., 2024b; Dalla-Toree et al., 2023), such as Evo (Nguyen et al., 2024a), LucaOne (He et al., 2024), have advanced DNA and protein modeling across the central dogma, they primarily target qualitative tasks. Consequently, the quantitative nature of expression, which the ultimate determinant of phenotype, remains largely unaddressed. This gap raises fundamental question: How can map the whole DNA sequence context-aware quantitative expression?
Answering this question requires addressing two key challenges: (1) Sequence length mismatch, as genes often span hundreds of kilobases while their protein products consist of only a few hundred amino acids; and (2) Context mapping ambiguity, since alternative splicing and isoform reuse create inherently one-to-many relationships between DNA and proteins.
To overcome these challenges, propose CDBridge, context-aware post-training bridge strategy that unifies pretrained DNA and protein models within a single framework. Unlike prior approaches, CDBridge integrates both sequence-level and tissue-level contexts, enabling simultancous qualitative and quantitative modeling. The framework proceeds in two stages: (1) Seq-context learning, where cross-omics connector with cross-attention maps DNA embeddings to protein representations, supported by a pplicing-inspires adaptive token merge that selectively compresses non- non-informative regions while preserving functional signals; and (2) Env-context learning, where a conditional decoder injects tissue embeddings to model tissue-specific expression, selectively activating Stage outputs under given contexts.
To rigorously evaluate central dogma modeling, we introduce GTEx- BrEx-Bennchmak, constructed from GTEX and Ensembl. In contrast to existing benchmarks like Enformer or Isoformer, UTEx-Benchmald forces models to resolve long-rang maagee dependencies by identifying critical exons across vast genomic distances, managing exon reuse across multiple isoforms, and predicting tissue-specific expression levels. This creates challenging and biologically faithful evaluation for central dogma modeling.
Our contributions are threefold:
context-award two-stage bridge strategy that enables cross-omics alignment with minimal paired supervision, supporting both qualitative functional tasks (e.g., protein segmentation) and quantitative expression-level prediction under varied contexts.
An adaptive token-merge mechanism that mimics biological splicing by selectively merging genomic sequences, reducing length disparity and highlighting informative regions for efficient and interpretable modeling.
new benchmark qualitative cross-omics alignment and quantitative tissue-specific prediction tasks.
for aissue-aware central dogma modeling, covering both
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Chromosome
Proteias
DNA with Long Context
Prokein Danahase
terom
baoeers
Tissue
RNA
Protein Model
Protein
Single-cell Model
Function & Expression
Dictionary
Protcin
0.9
DNA Model
Multi-omics Connector
Function Decoder
Conditional Decoder
Protein
Protein a
0.1
Gene Level
Function Level
Cell Level
Figure 1: Overview of the CDBridge framework for context-aware cross-omics modeling of the central dogma. CDBridge operates in two post-training stages built on frozen DNA and protein foun- dation models, which consists of two stages: (1) Seq-context learning, which identifies informative regions from long genomic sequences and maps them to protein-related functional representations. (2) Env-context learning, conditional decoder that incorporates tissue embeddings compose functional features into tissue-specife representations for gene expression prediction.
METHOD
We introduce CDBridge, a context-aware two-stage post-training framework that bridges single- omics DNA and protein foundation models under the guidance of the central dogma. By implicitly leveraging RNA as biological mediator, CDBridge aligns cross-modal representations through both sequence-level interaction and tissue-specific context modeling. A left-to-right overview of the architecture illustrated in Figure 1. Then we introduce our framework from stage to stage 2.
2.1 STAGE 1: MULTI-OMICS CONNECTOR FOR LONG-SHORT RANGE BRIDGING
The first stage of CDBridge tackles two core challenges in cross-modal representation alignment: (i) the intrinsic mismatch in sequence length, where full-length DNA sequence (~~00t tokens) encodes one or more relatively short protein sequences (~~00² tokens); and (ii) the semantic gap between modality-speciffi pretraining objectives: DNA embeddings capture genome-wide contextual signals, while protein embeddings focus on functional amino acid chains from localized coding regions, which can be seen in the Figure 2(a).
Framework. To bridge these gaps, design seq-context-aware cross-omics connector that projects the full DNA emhedding space into a functionally meaningful protein space using a cross- attention mechanism. Let X DNA R be the DNA embedding sequence produced by frozen DNA encoder, where L is the token length and d the embedding dimension. We further introduce learnable token dictionary Tprot E M initialized from k-means clustered protein embeddings across the training dataset. These tokens serve prototypes, which are treated as keys and values in the cross-attention module:
XDNA Vd
T prot
Attn XDNA, Tppos Tprot) = softmax
Tprot-
Token Compression via Merge and Recover. To efficiently handle the long-range dependencies in genomic sequences, we propose a biologically inspired token compression strategy based on ToMe (Bolya et al., 2023), mimicking transcript splicing mechanisms. This technique allows to focus computation on functionally relevant regions by adaptively merging non-critical tokens, thereby reducing sequence length while preserving semantic fidelity.
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UMAP Projection
Projection
UMAP Projection
DNK levo2) Protein (CSM2)
DMM-cdd (evo2] Protein (CSM2)
CDerddee (Ours) Protein (CSM2)
(a) DNA and Proteins.
(b) cDNA and Proteins.
(c) Ours DNA and Proteins.
Figure 2: Illustration of the challenges in aligning DNA and protein representations across modal- ities. (a) Even with advanced models such as Evo2 and ESM2, alignment remains challenging due to the inherent one-to-many mapping between long DNA sequences and their shorter protein counterparts. (b) Manually segmenting DNA into coding regions reduces input ambiguity, but fails to resolve the representation gap, as existing DNA models are pretrained full-genome data lacking isoform-specific supervision. (c) By introducing an adaptive coken-merging strategy. CDBridge effectively reduces the modality gap and enhances alignment between DNA and protein embeddings.
Predicted Functional Mask
Intron
Exrone C
Intron
Extron
X
x
X
Function Decoder
Selected Embodding
Unmerging
KV
Compreseed Festurex
Source
Protein
Conditional Decoder
Function Expression
Database
Procein
a.9
Feed Forward
Protein Model
Tissue Dictionary
Protein
0.3
Self Attention ToMe
MaxPool
Protein
Cross Attention
KV
MaxPool
Connector
Protein Dictionary
Single-cell Model
Bulk RNA on Tissues
DNA Model
Long DNA wio Annotations
(a) Stage I: Function Prediction
(b) Stage 2: Expression Prediction
Taking the DNA sequences with long contexts as input, whose embedding features are extracted from an existing DNA foundation model (Nguyen et al., 2024a), the multi-omic connector aggregates protein knowledge with cross- attention from the protein dictionary and compresses the fused features with al., 2023) while the function decoder predicts the protein functions with token-wise masks. (b) Stage 2: After selecting the fused embedding with the predicted masks, the conditional decoder achieves cell-level prediction of expressions of certain proteins with the tissue dictionary.
Figure 3: Two-stage training pipeline of CDBridge. (a) Stage 1:
(Bolya
Given DNA embedding sequence XDNA = (x R4xd we begin by randomly partition- ing the token indices into two disjoint sets, A and B. For each token E A, we compute cosine similarity with all tokens j E B and identify its most similar partner:
(x,,
(1) = arg max
JEB ||x;|| ||x;
If the similarity exceeds a threshold т. the pair (i)) is selected for merging. is determined by the pre-set merge ratio, which is randomly sampled from the Gaussian distribution for each input during training. The two tokens are merged via direct averaging:
1 (x: + +x)+())
x; = 2
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where the index the surviving token, discarding (1). This yields compressed sequence XDNA {&; E 8) RL'xe with T/' < T.. We maintain mapping function " ,.,.... I.} UU [NULL), where T() = if token / survives, and (1) = i if token / was merged into token i. In the unmerge phase, each discarded token is reconstructed by assigning the embedding of its surviving partner. The reconstructed sequence XDNA fx....... {x. is obtained as:
.....
X1 (()
if (1) = (token survived), if n (1) # (token was merged),
& =
which is then processed by a lightweight Transformer decoder predict functional regions. This This procedure can be interpreted as a variant of MAE(He et al., 2022), where the masked regions are adaptively selected based on inter-token similarity. Compared to standard MAE, this strategy is saliency-aware, preserves positional aligmment, and enables token-level supervision, making it especially well-suited for genomic sequences where functional signals sparse and localized. The alignment results are shown in Figure 2. It can be seen that our method shows a better alignment for the global DNA embeddings and the protein embeddings, even exceeds the pre-cropped cDNA and protein embeddings.
2.2 STAGE 2: CONDITIONAL DECODER FOR CONTEXT- EXPRESSION MODELING
While Stage focuses capturing the structural aligmment between DNA and protein through token-level interactions, Stage 2 addresses the variability of gene expression across diverse cellular environments. Specifically, this stage introduces tissue-aware conditional decoder designed to model the variability of gene expression, capturing the regulatory plasticity that arises from these contextual factors.
Tissue Dictionary Conditional Context. To incorporate relevant biological context, con- struct a Tissue dictionary Teavir E CxCx by leveraging a single-cell foundation model(Cui et al., 2024). Here, C represents the number of tissue types, M is the pre-set number of cell tokens, and d denotes the dimensionality of the embedding. Specifically, bulk RNA expression data is first passed through the single-cell foundation model and then pooled generate global embeddings represented by M tokens. Each tissue is represented by a vector t, E RAMx capturing its cell state. During training. tissue labels supplied conditional inputs for expression inference, enabling the model to learn differential regulation patterns across distinct biological environments.
Avoiding Information Leakage from the Context. The tissue embedding constructed via mean pooling over approximately 19k genes, without isolating expression values of the target gene its neighbors. This aggregation effectively dilutes individual gene-level signals. To further verify this, conducted control experiment in which the model was trained and tested using only the tissue embedding as input (excluding DNA features). In this setting, the l2 value dropped to nearly zero (see Table 4), confirming that the tissue embedding serves solely as a conditioning signal rather than as an independent predictive feature.
Conditional Decoder Architecture. The conditional decoder a Transformer module that takes the tissue vectors queries and performs cross- coss-attention over the compressed DNA representations XDNA obtained from Stage 1. The output of the Decoder includes M tokens, each representing the candidate isoform-related protein embeddings, which is formulated as:
[pm 1M ~ (Pm 1
İDNA tz).
The decoder outputs enable two types of predictions: (I) isoform-aware protein embeddings, enabling regularization through contrastive loss, and (2) scalar regression output estimating the quantitative expression level of the target protein under tissue condition c. This dual objective ensures both qualita- tive semantic alignment and quantitative expression estimation. Unlike typical expression models that treat cells as unordered gene sets, our decoder preserves gene-level context and sequence semantics while modeling tissue-dependent effects, yielding fine-grained and generalizable predictions.
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Table 2: Expression prediction performance with (t) and Spearman(1) across five specific tissues, along with averaged results over the full dataset. The best results in bold. Models are grouped into: (i) auxiliary sequence-only baselines, (ii) specialist expression baselines, and (iii) our cross-omics bridge. Isoformer (Official) relies on TSS- -aligndd data setting and is thus not directly comparable to our unaligned, long-sequence protocol.
Brain
Heart
Kidney
Liver
Stomach
Average
Model
Spear
11"
Spear
12
Spear
I
Spear
I1=
Spear
10"
Spear
Auteltary Sequence-only Baselines
DNABERT2 (Zhou et al., 2024)
-0.004 0.317
-0.005
0.304
-0.004
0,317
-0.001
0.328
0.001
0.333
-0.004
0.317
NTv2 (Dalla-Tame Tomr al. 2023)
-0.012 0.238
-0.03
0.317
.0.15
0.176 -0.02
0.291
0.005
0.306
-0.012
0.289
Evo2-7B (Brixi et al., 2025)
0.021
0.324
0.018
0.318
0.024
0.328 0.017
0,312
0.023
0.325
0.021
0.324
T LcaOnee (He al., 2024)
0.006
0.320
-0.001
0.300
0.007
0.324
.0,003
0.314
0.002
0.318
0.001
0.309
Specialist Expression Baselines
Entermer (Avsec al.,
0.124
0.117
0.122
0.108
0.127
AlphaGenome (Avscc ct al., 2025)
0.234
0.442
0.260
0.404
0.229
0.380
0.221
0.438
0.242
0.410
0,248
0.438
Isoformer (Official) (Garau- Tuis al, 2024)
0.505
0.525
0.560
0.530
0.515
0.530
0.720
Isoformer (wlo TSS Align) Lais et all, 2024)
-0.328
0.301
-0.303
0.269
-0.366
0.264
-0.268
0.312
-0.291
0.278
-0.315 0.309
Proposed Cross-omies Bridge Model
CDBridge (Ours)
0.421
0,708
0.346
0.687
0.327
0.594
0.382
0.631
0.410
0.673
0.387 0.618
3
EXPERIMENTS
We evaluate CDBridge across multiple biologically grounded tasks to assess its performance, general- izability, and interpretability.
3.1 DATASET CONSTRUCTION.
We construct the GTEx-Benchmale based on the GTEx v8 resource (Consortium, 2020), which provides matched genomic, transcriptomic, and proteomic annotations across 40 human tis- sues. For each protein-coding gene, we retrieve the DNA sequence and corresponding protein sequence from Ensembl (Cunningham et al., 2022), and pair them with tissue-specific RNA expression values and protein function anno- tations. We utilize strict split of 80% train- ing, 10% validation, and 10% testing based gene IDs prevent data leakage. To en- sure sequence manageability, genes with DNA sequences longer than 200k base pairs are ex- cluded. These ultra-long genes constitute only small long-tail portion (around 2% of genes in our statistics). The resulting dataset enables eval- uation on a wide range of biologically meaning- ful tasks, including Tissue-conditiones protein expression prediction, Coding region segmenta- tion, and Isoform- protein retrieval. More details are illustrated in the Appendix A.
Seen Unseen
Spearman Correlation
0.669
5,924
0.6
0.5
Stomachh
Testis
Thyraid
Uterus
Vagina
Figure Spearman correlation of gene-expression prediction across five tissues. Each bar reports the Spearman correlation between predicted and ground-truth isoform-level expression in spe- cific tissue. Model performance on unseen tis- sues closely mirrors that tissues observed dur- ing training, indicating robust cross-tissue general- ization and suggesting that the learned tissue beddings capture transferable regulatory patterns rather than overfitting specific training tissues.
Comparison with other methods. Table presents a comparison of representative mod- els across input modalities. biological reasoning capabilities, and task types. DNABERT-2 (Zhou et al.. 2024) is single-omics model trained exclusively on DNA sequences, focusing on sequence- level representations. Ævo2 (Brixi et al., 2025) incorporates aspects of the central dogma during training, enhancing sequence modeling, yet it remains fundamentally single-omics. Building upon these, LucaOne (He et al., 2024)supports multi-omics inputs spanning DNA, RNA, and protein, offering broader coverage. However, these foundational sequence models lack the capacity for tissue- conditiond reasoning or quantitative expression prediction. In contrast, specialist models such as Enformer (Avsec et al., 2021) and Isoformer (Garau-Luis et al., 2024) are explicitly designed for expression prediction. However, they rely on dieedddiension output heads and therefore structurally cannot perform zero-shot prediction unseen tissues without retraining heads. Concurrently,

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AiphaGenome (Avsec al., 2025) is designed for nucleotide-level quantitative prediction from long DÑA contexts across multiple omics. Nevertheless, emphasizes static averaged outputs for each DNA imput, overlooking dssue-deeenddee
variations. Uniquely, CDBridge enables comprehensive cross-modal alignment while performing quantitative expression modeling conditioned on tissue contexts. This capability positions CDBridgee versatile biological foundation model, capable of fine-grained, cuvironment-awant inference across multiple molecular modalities.
3.2 TISSUE-AWARE GENE EXPRESSION PREDICTION
We evaluate CDBridge the challenging task of cessiiccodd isoform-level expression pre- diction using the GTEx dataset, comparing it against general representative sequence-only models (auxiliary sequence baselines) and specialist models designed for expression prediction. The features of the auxiliary sequence baselines are frozen and followed by a trainable layer for expression regression. Two evaluation settings are considered: (i) seen-tissue (Table 2), where tissues appear during training but test DNA sequences are unseen; and (ii) unseen-tissue (Figure 4), where entire tissue types as well the test DNA sequences are held to simulate zero-shot generalization. Specifically, we employ a leave- save-tissue-out protocol: the model is trained on 90% of tissues and evaluated exclusively on genes from the 10% held-out, unseen tissue types.
From Table 2, sequence-only models such as DNABERT-2 and Evo2 show limited performance since they lack explicit modeling of tissue-specific regulatory signals. While Enformer, Alpha and Isoformer incorporate expression prediction, they rely on tissue-specific classifiers trained solely on DNA inputs, limiting their ability to generalize to unseen tissues. contrast, CDBridge consistently achieves superior &2 and Spearman scores across diverse tissues, with particularly strong gains in contexts where regulation is highly dissue-dependent. Figure 4 further demonstrates that CDBridge sustains robust performance even under imseen-tissus conditions. It is setting unsupported by Enformer or Isoformer, validating its ability to capture transferable context patterns through its two-stage, context-aware architecture.
3.3 CROSS-OMICS DOWNSTREAM TASK.
We evaluate the multi-omics representation capacity of CDBridge through three challenging down- stream tasks: coding region segmentation, isoforn retrieval, and DNA-protein association. Full protocols are provided Section A.2, and results are summarized Table
Coding Region Segmentation. This task evaluates whether the model can identify which DNA segments code proteins, a key step for genome annotation and clinical variant interpretation. As shown in Table 3. single-omies models such as DNABERT-2 and Evo2 perform relatively poorly due to their inability to disambiguate coding signals from long genomic sequences, while LucaOne benefits from multi-omics embeddings achieve better alignment. CDBridge outperforms them, demonstrating its ability to capture Isoformer-relate, protein signals for fine-grainee token-level tasks.
Isoform Retrieval, Given a set of candidate isoforms, this task assesses the ability to retrieve the top-K activated isoforms from DNA sequences with different conditions. It is important for disease mechanism discovery. As expected, unimodal DNA models perform poorly. LucaOne, while benefiting from multi-omics embeddings, still provides only moderate performance due to the absence of fine-grained isoform-level modeling. In contrast, CDBridge achieves the best results, leveraging modal alignment and tissue-aware conditioning to capture tissue-specific isoform usage patterns accurately.
Central Dogma. This binary classification task evaluates DNA-protein pair is functionally associated, relevant to disease mechanism discovery and drug target identification. The Central Dogma dataset from LucaOne is used. DNABERT-2 lacks the capacity to incorporate protein information, yielding weak performance. Evo2 and LucaOne performs better, benefiting from joint modeling, but lack task specialization. CDBridge achieves the highest performance by leveraging multi-omics conditioning and structured alignment.
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Table 3: Comprehensive evaluation across three biological tasks (best bold, second-best underlined).
Coding Region Segmentation
Isoform Retrieval
Central Dogma
Model
Acc 1
A
F1 T
Acc t
MRR
Acc f
AUC 1
F1
Random
0.617
0.269
0.002
0.010
0.181
0.503
0.499
0.502
Single-omics General models
NTv2-500M (Dalla-Turee Torre al., 2023)
0.814
0.529
0.134
0.145
0.229
0.572
0.597
0.415
DNABERT-2 (Zhou et al., 2024)
0.851
0.612
0.382
0.132
0.227
0.574
0.598
0.482
Evo2 (Brixi al., 2025)
0.993
0.848
0.597
0.191
0.278
0.672
0.725
0.518
Muti-omics General modely
LucaOne (lle et al., 2024)
0.993
0.859
0.613
0.259
0.354
0.714
0.767
0.545
CDBridge (Ours)
0.995
0.993
0.635
0.337
0.4.6
0.742
0.792
0.568
ENSC00000136243
DNA Mask
Merged
Token
ENSG00000280969
Mask
Merged
Token
Preserved region
Figure 5: Token merging aligns with functional regions. The merging process selectively retains tokens associated with coding regions, while non-coding segments are predominantly merged. This pattern arises without explicit exon masks in the loss, indicating that CDBridge learns to allocate computation biologically salient regions data-driven and interpretable manner.
3.4 INTERPRETABLE ANALYSIS
Adaptive merge salient regions. We visualize the token merging behavior by overlaying the merging heatmap with known exon/intron annotations. The experiment conducted on held-out test samples, where we visualize the top 100 most frequently reused tokens during the adaptive token merging stage. As shown in Figure 5, without explicit masking for merging, the model consistently retains tokens corresponding to coding regions (exons), while aggressively merging tokens in non-coding regions (introns or intergenic). This behavior suggests that CDBridge effectively allocates computation to biologically significant regions in a data-driven and interpretable manner.
Tissue-aware activations. The activated tokens under different conditions are visualized in Figure The first gene sample activates tokens predominantly associated with the first two isoforms, and the activation patterns shift based on tissue type, reflecting expression differences. Similarly, the second gene shows consistent activation in tokens 4-16 across tissues, but with varying intensities. These patterns indicate that CDBridge is capable of modeling the tissue-specific regulatory contexts.
vs Heatmap
Heatmap
Tiesue
Figure 6: Tissue-specific token activations. Fach panel displays the token activation magnitude for specific isoforms (rows) across various tissue contexts (columns). CDBridge selectively activates Ssoform-associnn tokens, with varying intensity across tissues, reflecting its capacity to model tissue-aware gene expression across different DNA regions.
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Table 4: Ablation study of CDBridge components. Each row represents a different configuration of Stage I (segmentation) and Stage 2 (expression prediction). vf indicates the corresponding component enabled. Segmentation performance is measured by AUC and F1, while expression prediction is evaluated using Rt and Spearman correlation. Numbers in (+A) show relative improvements over baseline.
Stage 1
Stage Tissue Clust
Segmentation
Expression
ToMe Attn. Fixed Clust Learned Clust
AUC f
F1
R 1
Spear
X
X
0.848
0.600
0.021
0.324
x
X
0.882
0.601(-0.00)
(-.084)
(-457()0.119))
X
X
0.910:-0.12)
0.602/00.00)
0.212
(.483(+0.159)
/
/
X
(0..44))
0.635 (0.035)
(..15)
(o.15))
X
X
/
0.020
0.128;-0.19)
/
0.993
0.635(.0.035)
(..66
(o.24)))
3.5 ABLATION STUDY
To understand the contribution of each component in our two-stage CDBridge framework, we conduct systematic ablation experiments along both segmentation and expression pathways (Table 4). The first stage includes ToMe module and protein clustering module, with or without learning. The second stage evaluates the presence tissue-specific clustering during expression prediction.
In the segmentation task, removing the entire Stage 1 (i.e., ToMe or protein clustering) leads to significant drop in performance (AUC = 0.848, Fl 0.600), matching the Evo2 baseline. Introducing ToMe alone slightly improves segmentation, suggesting the benefit of adaptive compression. Adding a non-learnable protein cluster yields moderate gains. while learning the protein cluster jointly with ToMe provides the best segmentation performance. For the expression task, disabling tissue condi- tioning results in poor generalization (R²= 0.020, Spearman = 0. 128), confirming that incorporating single-cell models for tissue-aware modeling will not introduce information leakage. Enabling all components achieves strong improvement (R²² 0.387, Spearman = 0.618), validating the design of the two-stage architecture.
4 RELATED WORK
Pretrained Biological Language Models. Recent years have witnessed rapid progress in large-scale pretrained models for biological sequences. On the molecular level, protein language models such as ESM (Rives et al., 2021), Evo (Meier et al., 2021), and AlphaFold (Jumper et al., 2021) have shown impressive performance in protein structure prediction. function classification, and representation learning. On the genomic side, models like Enformer (Avsec et al., 2021) and Nucleotide Trans- former (Dalla-Torre et al., 2023) leverage long-range attention to model cis-regulatory elements and gene expression from raw DNA sequences. While these models excel within their modalities, they are typically trained independently and lack mechanisms for aligning representations across the molecular hierarchy from DNA protein, limiting their utility in tasks that require reasoning the central dogma.
Multi-omics Modeling of the Central Dogma. To bridge across DNA, RNA, and protein, multi- omics models such as CD-GPT (Zhu et al., 2024), GENA-LM (Ji et al., 2023), Life-Code (Liu et al., 2025), and LucaOne (He al., 2024) have been proposed to unify biological sequences in shared embedding space. These models usually decompose input sequences into functional segments (e.g., coding vs non-coding) and attempt modality alignment via paired training. Some focus on qualitative modeling like function transfer, while others explore quantitative prediction of gene expression. However, most overlook crucial biological context: (1) splicing and regulatory mechanisms often cause one gene to yield multiple proteins, and (2) the same DNA may lead to different expression outcomes depending on the cellular or environmental context. These factors make DNA-to-protein alignment inherently ambiguous and context-dependent, a challenge under-addressed in existing models. In contrast to unified multi-omics foundation models like GENA-LM and LucaOne, which require end-to-end multi-omics pretraining, CDBridge operates post-training bridge that augments frozen single-omics DNA and protein encoders with dissue-awrr cross-omics reasoning, avoiding expensive retraining while retaining the flexibility of modular single-omies backbones.

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Multimodal Connectors and Bridge Strategies. Given the high cost of collecting large- scale multimodal datasets, a promising direction is to bridge pretrained single-omics models through lightweight post-training aligmment. Existing efforts in general multimodal AI (Radford et al., 2021; Chen et al., 2024; Xue et al., 2024; Li et al., 2023; Alayrac et al., 2022) have shown success in domains like vision-language, but biology poses unique challenges such extreme sequence length disparities and complex one-to-many mappings (e.g,, splicing, RNA editing). Few biological models have explored connector-based strategies that are both context-aware and biologically grounded. In this work, we propose CDBridge, a post-training bridge framework that incorporates both sequence and cellular context, aligning existing DNA and protein models support realistic, condition-awe -aware aware biological tasks.
5 CONCLUSION
We present CDBridge, a biologically grounded and context-aware framework that bridges single- omics foundation models through a two-stage design inspired by the central dogma. By leverag- ing ToMe-based token compression to capture isoform-aware coding structures and introducing conditional decoder to model tissue-specifie regulation, CDBridge enables fine-grained, condition- dependent protein expression modeling directly from DNA. Our approach integrates both structural semantics and environmental context, outperforming existing models that either ignore genomic continuity lack regulatory awareness. CDBridge not only establishes scalable method for DNA-to-protein reasoning but also opens new avenues for complex biological systems.
Limitations Despite its promise, CDBridge still exhibits several constraints. First, the framework relies on high-quality isoform annotations and tissue-resolved expression atlases; incomplete or noisy metadata can propagate errors through both stages. Second, limited by the scarcity of publicly accessible fine- fin-grained contextual data, our conditional decoder currently models tissue context as categorical variable, leaving unaccounted finer- meer-grained factors such as developmental stage, disease state, and miiroenniirommentll cues. Third, while ToMe compression mitigates sequence length, end-to-end training on whole-genome inputs remains computationally demanding, limiting scalability to large cohorts or non-human genomes with less curated references. Future work could relax the reliance on curated isoform annotations by incorporating transcript assembly or junction-lever supervision, and extend the conditional decoder model continuous or spatially resolved contexts (e.g., developmental time, disease severity, or spatial micro-environments).
ACKNOWLEDGMENTS
This work was supported by Open Research Fund of The State Key Laboratory of Multimodal Artifi- cial Intelligence Systems (N0.MAIS2025064), National Science and Technology Major Project NN.22022ZD0111001) National Natural Science Foundation of China Project (No.624B2115 No. No.U21A20427), Project (No. WU2022A009) from the Center of Synthetic Biology and Inte- grated Bioengineering of Westlake University, the Hangzhou Postdoctoral Daily Funding Program (No. 16.1103140026582000, 2025) and InnoHK Program.
Reproducibility Statement. We have made significant efforts to ensure the reproducibility of our work. Detailed descriptions of the model architecture, training objectives, and evaluation protocols are provided in Sections and 4 of the main text, with additional implementation details. hyperparameters. and dataset statistics included the Appendix and supplemental materials. Upon acceptance, we will publicly release the full FTTEx-Benchmark dataset splits and preprocessing scripts, as well the CDBridge codebase and pretrained models, to enable end-to-end reproduction of experiments. This will ensure that other researchers can directly validate our results and extend our framework to new settings.
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A GTEX-BENCHMARK
We introduce GTEx- BrEEx-Benchmar, a comprehensive dataset designed to evaluate models of the Central Dogma through both qualitative and quantitative tasks. Our goal is to provide cross-omics benchmark that adheres to biological principles, specifically the DNA + RNA 7 protein flow-and captures tissue-specific regulatory complexity.
For each codiing gene, we extract genomic DNA sequences and their corresponding protein products from Ensembl (Cunningham al., 2022), and pair them with tissue-resolved RNA expression profiles from GTEx-v8 (Consortium, 2020). Additionally, we include protein-leve annotations such as coding region mappings to support functional analysis. This enables two key task types: (1) qualitative alignment, such isoform-level protein retrieval given DNA; and (2) quantitative prediction, such as tissue-specific gene expression modeling. To broaden evaluation, we also incorporate the Central Dogma subset from LucaOne (He et al., 2024), which provides curated DNA-protein pairs with explicit alignment.
DNA Sequence Length Distribution
Protein Sequence Length Distribution
15.3%
6.0%
8.6%
27.0%
Interval
13.8%
Interval
10.6%
500-1000
20.9%
1000-2000
100-200
2000-4000
200-100
400 800-800
12.2%
9.9%
5000-16000
16000-32000
25.4%
7.9%
32000-100000
3200-10000
9.3%
7.7%
(a) DNA length distribution.
(b) Protein length distribution.
Distribution of protein count per DNA
8000
Mean: 5.7
Median: 4.0
7000
6000
DNA count
5000
4000
3000
2000
1000
10
40
50
70
Protein count
(c) Protein counts per DNA.
Figure 7: Sequence statistics the
dataset.
A.1 DATA STATISTICS
Sequence Distribution. coding and non-coding DNA regions. Owing to alternative splicing, single genomic sequence can give rise multiple transcript variants, resulting in diverse protein isoforms with distinct functional roles. To ensure computational tractability, we exclude genes with DNA sequences longer than 200,000 nucleotides. We divided the dataset into training set, validation set. and test set in the proportions of 80%, 10%, and 10%, respectively. Figure 7 illustrates key statistics the dataset: (a) the distribution of DNA sequence lengths, (b) the distribution of protein sequence lengths, and (c)
comprises over 19,000 human genes, each containing both
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the number of protein isoforms associated with each gene. These figures underscore the substantial sequence and isoform-level heterogeneity that models must handle when reasoning across omics layers.
Tisse expression preprocessing. The GTEx project provides gene expression measurements across 49 human tissues, enabling context-dependent modeling. To ensure comparability, nor- malize transcript-level expression values using TPM (Transcripts Per Million) and apply a log transformation, (Tg( 1), for numerical stability during training. We define transcript as activated in a given tissue if its TPM exceeds 0.1. Based on this threshold, we compute the number of activated protein isoforms per gene across tissues. As shown in Figure 8, the average number of activated isoforms per gene is approximately five, underscoring the complexity introduced by specific expression and splicing.
Average Protein per Tissue
Adposs Suzcitsneou
Adipss
(0mentur)
Adrenal
Antyy
Artary Carorary
Anery Tibll
Cm
Bluin
Hammary
tesphagus Gettreesphaggs)
$.08
Tosuee
5:33
5.24
5.07
5,30
3.16
3.20
5.27
Prodtate
5:06
San Sun Reponed (Spprapublc)
5.09
Sun (lower
5.10
Small Irdeatine Semiina lsum
5.65
5.11
Storach
5.33
lola
4.85
Thyrad
5.10
Uters
5.14
Vigina
5.08
Average Number Proteins per DNA
Figure 8: Distribution of the number of activated protein isoforms per gene across tissues in GTEx- Benchmark.
A.2 EVALUATION PROTOCOL AND DOWNSTREAM TASKS
To evaluate the generalization and cross-modal reasoning capabilities of CDBridge, consider three biologically grounded downstream tasks: Coding Region Segmentation, Protein Retrieval, and DNA-Protein Association Classification. These tasks are designed to reflect different aspects of the central dogma, ranging from token-level sequence understanding to modality-spanee and functional prediction, and follow the same evaluation setup as Section 3.
alignment
Coding Region Segmentation. Each genomic locus is provided in full length (up to 200 kb), and the model predicts whether each nucleotide belongs to a canonical coding sequence. Ground-truth labels obtained from Ensembl CDS annotations. To reduce label noise at exon-intron junctions, positions within +3 bp of splice boundaries are ignored during metric calculation. We evaluate with accuracy, AUC, and F1, where AUC is reported both globally (pooled nucleotides) and averaged across loci, ensuring that performance is not dominated by a few long sequences.
Isoform Retrieval. We construct tine-tissue pairs from GTEx by selecting genes with at least two expressed isoforms tissue. The tissue is conditioned with scGPT (Cui et al., 2024) and is concatenated with DNA embeddings as input. Each query contains candidate isoform set, and the
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model is asked rank them according to predicted expression in the given tissue. Ground-truth usage is defined by the median TPM across GTEx donors. Performance is measured by Acc@3, the fraction of cases where the true top isoform falls in the top three predictions, and MRR, the mean reciprocal rank of the top isoform:
1 N
MRR
rank;
where rank, is the predicted rank position of the ground-truth top isoform for the i-th gene gene-tissue pair, and N the number evaluation pairs. This protocol reflects practical applications such as pinpointing tissue-dominant isoforms for disease mechanism studies, where ranking few plausible candidates is more valuable than identifying a single absolute prediction. Compared to standard expression regression, this setup directly tests whether model can resolve isoform ambiguity under changing cellular environments.
DNA-Protein Association (Central Dogma Classification). we adopt the Central Dogma subset from LucaOne (He et al., 2024), which links genomic DNA segments to their translated proteins. Positive pairs are derived from canonical gene-isoforn mappings, while negative pairs are generated by sampling proteins from other genes matched by length and chromosome to avoid trivial cues. The dataset contains around 20k pairs with bass-balanced sampling. We report accuracy, AUC, and F1. This task requires the model to capture functional correspondence across modalities rather than simple sequence similarity, and is directly relevant to applications such as drug target validation, where identifying functionally matched DNA-protein pairs critical.
cosine similarity
1.00
Adipose Suhrutanesss
Adipose Visceral (Omentum)
Adrenal Gland
Artery Aorta
Artery Cerenary
0.75
Artory- Total
Blladde
Brain Cerebellum
Endocorvis.
Colon Sigroid
Colon tranverrse
Esophagus Gastroesophages Junction
Esophaggia is Mucosa
Frophegian i Musculer -
follopian Tube
Heart: Abrial Appendage
Heart Ventricle
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0.000
Kidney
Minor Salivary Sland
Muscle Skeletal
-0225
Nerve lobel
Ovary
Rancreas
Raatbry
Prostato
Skin Not Sun Exposed (Suprasubic)
siin Sun Exposed (Lower leg)
Small intestine Terminal fluu
Spleen
lest
Thyrold
Utenus
Vagina
Adiport Suared ctaronut
Artery fatory Tidal
Breast. Momnie mnar peveever
Brain teoeell Bain
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Mit bitl aert Soreeeer
-1.00
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Figure 9: Cosine similarity heatmap of tissue embeddings generated by scGPT (Cui et al., 2024). Related tissues (e.g., arteries) cluster together, suggesting biological consistency. This structure supports the use of scGpe-derived tissue embeddings as conditioning signals in CDBridge, since they preserve biologically meaningful relationships between tissues.
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B DETAILS OF THE METHOD
B.I TISSUE EMBEDDING.
To support generalizable cross-tissue prediction, we incorporate tissue embeddings derived from the cell foundation model scGPT (Cui et al., 2024). For each tissue, we extract its representation from scGPT and apply mean-centering across all tissues by subtracting the average embedding across dimensions. This debiasing step enhances inter-tissue contrast and facilitates better context conditioning in downstream tasks. Figure 9 shows the cosine similarity heatmap among the resulting tissue embeddings. As expected, biologically related tissues such as Artery-Aoria and Artery- Coronary, which belong to the same higher-level anatomical category, exhibit stronger similarity. This structure reveals that scGPT captures semantically meaningful and biologically consistent relationships between tissues, supporting its use compact and transferable tissue representation.
B.2 MERGE RATIO
Merge ratio indicates the percentage of tokens merged during the forward pass. During training, the merge ratio is sampled dynamically for each batch from clipped Gaussian distribution: ~ NN 0.375, a = 0.1), clipped to range [0.25, 0.50]. We employ this dynamic sampling strategy as form of structural data augmentation. By forcing the model to perform segmentation and alignment under varying degrees of compression, we prevent it from overfitting to specific sequence strides or fixed resolutions.
B.3 Loss FUNCTIONS
CDBridge adopts a two-stage training paradigm. Stage focuses on qualitative token-level alignment between DNA and protein isoforms. while Stage 2 performs quantitative, tissue-awar prediction of protein expression levels. The overall training objective combines classification and contrastive components.
Stage 1: Multi-omics Connector for Long-Short Range Bridging. This stage optimizes two complementary losses:
(I) Multi-label Binary Classification Loss. To supervise the coding region segmentation, binary cross-entropy loss over the predicted probability of each DNA token coding for any protein isoform. The loss is computed as:
Cets BCEWithLoogissLoss(eeee Vuuss),
where Upredd are the model logits and Ymusk is the binary mask indicating coding tokens for each isoform.
(2) Cross-modal Token-level Contrastive Loss. To enforce fine- fine-grained alignment between DNA and protein token embeddings, employ batch-wise self-supervised contrastive loss inspired by SimCLR et al., 2020). For each aligned DNA-protein token pair (as defined by the binary mask), maximize similarity and simultaneously push apart negative DNA samples selected randomly within the same sequence. Specifically:
Calign
1
Σ
log
exp (sim( (2f,2222)) 2(1)T)
exp (sim(2], 2/)/T) Σk_ exp (sim( (2], 2 (d/)))
where 2!" and 2d are the normalized embeddings the i-th aligned protein and DNA token, 2 are randomly sampled negative DNA embeddings, and is a temperature hyperparameter. The similarity function sim(..) is implemented as the dot product of n.2-normalized vectors.
The final loss for Stage is the sum of both components:
Lstagel Lels + Calign
where >atign is hyperparameter controlling the strength of the global alignment.
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Stage 2: Tissue-aware Expression Regression. In the second stage, the model is trained to predict protein expression levels tissue-aware manner. The imput is the merged DNA representation from Stage conditioned tissue embeddings. The regression task supervises quantitative mapping from DNA to protein isoform expression values under different tissue conditions.
(I) Tissue- Regression Loss. For each protein isoform, extract its expression value under the corresponding tissue index and regress from the model's 's output. The loss is formulated as:
B
K (in:
Lexpe BXK
yax)²
k=1
where B is the batch size, K is the number of proteins, Du is the predicted expression, and yıu is the ground-truth expression under the sampled tissue.
(2) Global Embedding Consistency Loss. To enhance representational alignment across tissues and protein isoforms, we added a global contrastive loss to enforce the consistency between the pooled DNA embedding and the protein representation. This prevents overfitting to token-level patterns and encourages global biological coherence. We use a symmetric contrastive loss defined as:
1
B
exp
(sim(2 (2
1 proteing //T
Gglobal
log
B
Σ
B j=1
exp
sim (2) ,2)
protsin
=; where = and protein denote the pooled (e.g,, and r is the temperature.
CLSS-token) global embeddings for sample i,
The total loss for Stage 2 training is the sum of both:
Lsuge2 Lexpe + Aglobal Cglobal,
where Xglobal is a hyperparameter controlling the strength of the global alignment.
C MODEL PARAMETERS AND TRAINING SETUP
CDBridge is built on top of powerful pre-trained single-omics models to facilitate tissue-aware cross-omics bridging. Specifically, we use the 7B Evo2 model (Brixi et al., 2025) as the DNA encoder, the 650M ESM2 model (Lin et al., 2022) for protein representation, and scGPT (Cui et al., 2024) to provide tissue embeddings.
Training Strategy. The training process divided into two stages. In Stage I, both the encoder and decoder are trainable to learn fine-gras grained DNA- alignment and coding region segmentation. In Stage 2, we freeze the encoder and decoder components from Stage preserve the learned cross-modal alignment. A new tissue-aware decoder is introduced to regress tissue-specific protein expression levels. This separation ensures that the model retains sequence-level alignment while learning contextual regulation patterns for expression.
Trainable Components. CDBridge built upon pre-trained single-omics models to enable tissue- aware cross-omics prediction. Specifically, we utilize the 7B Evo2 model (Brixi et al., 2025) as the DNA encoder, the 650M ESM2 model (Lin et al., 2022) for protein representation and alignment, and scGPT (Cui et al., 2024) for deriving tissue embeddings. Table summarizes the trainable components and their parameter counts across both training stages. In Stage 2, the encoder and decoder from Stage are frozen to preserve learned alignment, and only the tissue-aware decoder and expression regression layers are updated during training.
D MORE ABLATIONS
As shown in Table 6, present additional ablation results evaluating CDBridge's performance when integrating features from various pretrained models. As shown in the table below, the combination of Evo2 and ESM2 achieves the best overall performance across all three tasks. Additionally,
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Published as a conference paper at ICLR 2026
Table 5: Trainable parameter counts for key modules.
Module
Params
Notes
ToMe-baasd cross-omics omics encoder
~M0..4M
Frozen in Stage
Transformer decoder (Stage I)
20.7M
Frozen in Stage
Tissue Conditional Decoder (Stage 2)
~ 8.3M
Trainable in Stage 2
Classification Head (Stage 1)
415K
Frozen in Stage
Regression Layer (Stage 2)
513K
Trainable in Stage 2
Table 6: More ablation studies and comparison results across GTEx expression prediction and its two sub-tasks, where bold denotes the best results.
Coding Region Segmentation
Isoform Retrieval
GTEx Expression
Model
Acc f
AUCT
FI
Acc
MRR 1
Spear f
Random
0.617
0.269
0.002
0.010
0.181
13.027
0.005
NTV2-500M (Dalla-Tioe Torre et al., 2023)
0.814
0.529
0.134
0.145
0.229
-0.012
0.289
DNABERT-2 (Zhou al., 2024)
0.851
0.612
0.382
0.132
0.227
-0.004
0.317
Evo2 (Brixi al., 2025)
0.993
0.848
0.597
0.191
0.324
LucaOne (He
2024)
0,993
0.859
0.613
0.259
0.354
0.001
0.309
CDBridge (NTv2, ESM2)
0,912
0.876
0.572
0.197
0,271
0.371
0.579
CDBridge (DNABERT-2, ESM2)
0.950
0.613
0,304
0.375
0,583
CDBridge (Evo2, ESM3)
0.993
0.989
0.616
0,339
0.434
0.382
0.607
CDBridge (Evo2, ESM2)
0.995
0.993
0.635
0.337
0.436
0.387
0.618
integrating multiple pretrained features clearly improves upon single-modality baselines. Although ESM-3 employs VQ-quantized features incorporating amino acid sequences, protein structures, and functional annotations (thus excelling at the protein retrieval task), slightly underperforms ESM-2 in CDS segmentation and expression prediction, likely due to reduced compatibility with DNA-sequence-levee detail required by these tasks.
Besides, we performed an additional ablation study comparing the performance of using a fixed clustering mechanism versus our learned clustering mechanism within the full Stage 2 model. This comparison validates the necessity of the learned, data-driven approach for optimal performance. The results are summarized Table 7. The significant performance improvement observed with the learnable mechanism confirms that data-driven cluster assignment is crucial for effectively integrating sequence and tissue context information.
Table 7: Fixed vs. Learned Clustering on GTEx Expression Prediction.
Clustering Mechanism
R 2
Spearman
Fixed
0.305
0,472
Learnable
0.387
0.618
E COMPARISON WITH ADDITIONAL SOTA BASELINES
To further validate the effectiveness of CDBridge against state-of-tth-art sequence-based models, we conducted a comparison with Borzoi (Linder et al., 2025), recently proposed successor to Enformer. Similar to Enformer, Borzoi shares the fundamental fixed-output architecture designed for predicting epigenomic tracks across the genome.
As summarized in Table 8, while Borzoi demonstrates improved performance compared to Enformer (refer to main text), it remains significantly below CDBridge on our isoform-level, tissue-conditioned benchmark (R² of 0.201 vs. 0.387). This performance gap reinforees observation that fixed- output architectures struggle with the dynamic, context-conditionel modeling required for resolving isofori- expression across diverse tissues.
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Published as a conference paper at ICLR 2026
Table 8: Performance comparison with Borzoi on GTEx Expression Prediction.
Model
R²
Spearman
Borzoi (Linder et al., 2025)
0.201 0.387
0.336
CDBridge (Ours)
0.618
Isoform
a
Brain Lissue
Muscle Tissue
Isoform
NP 001330313
Gene ST C2545
Gene
SLC25A45
Isoform NP 001339311
Aligned via tissucc info
One-to-many
Liver Tissue
Isoform NP_001330312
unclcar alignment
Hard alignment between Gene and Proteins
Tissue-conditioned Alignment
Figure 10: Tissue context emables meaningful gene-protein alignment. (Left) Without tissue information, SLC25A45 (red) and its four isoforms (green) appear dispersed in different directions and weakly connected. Direct gene-to-isoforn alignment is ill-posed in this setting. (Right) Conditioning tissue context (Brain, Liver, Muscle) resolves this ambiguity by splitting the gene into tissue- specific representations, which form tight clusters with their corresponding isoforms. Solid edges indicate strong, context-aware alignments. The gradient arrow illustrates the transition from an unconditioned to context-resolved configuration.
ILLUSTRATIONS FOR -OMISS ALIGNMENT
Due alternative splicing in eukaryotes and gene reuse in prokaryotes, the relationship between DNA and proteins is one-to- many. As shown in Figure 10 (Left), gene can produce multiple isoforms whose expression levels vary across tissues, meaning the same DNA sequence may serve different functions in different contexts. An ideal bridging strategy, as illustrated in Figure 10 (Right), should align DNA to different context-specific centers, enabling tissue-aware interpretation.
G USE OF LLM
We use large language model (LLM) for minor edits to grammar, phrasing, and readability. The LLM not involved in designing the method, developing theoretical results, or conducting experiments. All technical contributions, equations, and results are solely the work the authors.
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Reviews:
[{"review_id": "tj4NkZxB3q", "reviewer": "Reviewer_s2Bn", "summary": "**Problem (P)**\n\nThe paper asks how to map whole-DNA to context-aware expression. Current systems either model tissue context from gene IDs without DNA, or model DNA without tissue, and most cross-omics pipelines ignore isoform reuse and splicing. The authors call out two challenges: long DNA must be compressed (because proteins generally have fewer amino acids), and the same DNA strand may map to multiple proteins depending on tissue. \n\n**Solution (S)**\n\nCDBridge is a post-training two-stage bridge that keeps large DNA and protein models frozen and learns a light connector.\nStage 1 maps DNA embeddings to a protein prototype dictionary and an adaptive token-merge (ToMe) “splicing-like” compressor keeps informative regions, then a small decoder predicts coding-region masks.\nStage 2 is a conditional decoder, which injects tissue embeddings (from a single-cell FM) to produce tissue-specific isoform embeddings and a scalar expression prediction.\n\n**Contributions (C)**\n\n1. A two-stage bridge that aligns DNA and protein while conditioning on tissue for expression.\n\n2. A splicing-inspired token merge that compresses long DNA while preserving functional tokens.\n\n3. GTEx-Benchmark, pairing DNA, proteins, and tissue-resolved expression for three tasks: expression prediction, coding-region segmentation, and isoform retrieval. \n\n**Tasks and metrics (T)**\n- Tissue-conditioned expression regression: R² and Spearman, in seen-tissue and unseen-tissue splits.\n- Coding-region segmentation: Acc, AUC, F1 at token level.\n- Isoform retrieval: Acc@3 and MRR. Also a Central-Dogma binary DNA–protein association task.", "questions": "For my critical questions, kindly see the weaknesses. Given these clarifications in an author response, I would be willing to increase the score. \n\nAppendix C Table 5. Is stage 2 really 513 params?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This design is highly practical and scalable. It allows the framework to leverage the billions of parameters and complex representations learned by FMs without the immense cost of full retraining. \n\n2. The paper uses domain-specific biological knowledge to inform its architecture instead of just apply generic ML techniques. The \"splicing-inspired adaptive token merge\" (Section 2.1) is a good example.\n\n3. A common weakness in modeling is testing only on data similar to the training set. This paper's evaluation is stronger because it explicitly tests for generalization to entirely unseen biological contexts.", "weaknesses": "1. A methodological weakness in \"Dataset Construction\" (Section 3.1), where the authors state, \"genes with DNA sequences longer than 200k base pairs are excluded.\" Many of the most complex and important human genes, like Dystrophin (over 2 M base pairs), are far longer than this 200k cutoff.\n2. While framework's \"post-training\" design, while efficient, does it also inherit any/all biases, gaps, or blind spots from the FMs it bridges (Evo2 and ESM2). The author's view on this would be appreciated. \n3. The current approach, which essentially uses a single label (e.g., \"Brain\"), fails to capture finer-grained, critical factors that govern gene expression. Although acknowledged, could the authors clarify whether these factors were omitted primarily due to GTEx labeling constraints, modeling choices, or computational limits?\n---\n**Minor Issues (for the Authors; not weaknesses)**\n\n1. The paper already notes that Isoformer (Official) numbers come from their paper with TSS alignment. Consider adding a brief sentence that scores are not directly comparable to your setting to avoid misinterpretation.\n2.  \"maps\" --> \"map\", page 1\n3. \"remainslargely\" --> \"remains largely\", page 2\n4. “perform relative poor” --> “perform relatively poorly”, section 3.3\n5. \"actviated\" --> \"activated\", section 3.3\n6. \"s while the regions belong the non-coding regions\" This is an awkward sentence, figure 5\n7. \"Envir-context learning\" or \"environment-context learning\" Please choose one and be consistent\n\nI think I saw a couple more while reading. Kindly consider taking the help of LLMs to mitigate these.", "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": "Fbtm3vnsHz", "reviewer": "Reviewer_DSq4", "summary": "CDBridge is a two-stage post-training strategy that unifies pretrained DNA (Evo) and protein (also from the Evo family) foundation models for context-aware modeling of gene expression. \nStage 1 aligns DNA and protein embeddings using a splicing-inspired token merge mechanism (based on ToMe) to handle long-range dependencies.\nStage 2 uses a conditional decoder to inject tissue embeddings for environment-specific expression prediction.\nA new GTEx-Benchmark is proposed to evaluate models on tissue-aware central dogma tasks. \nCDBridge achieves strong results in expression prediction (R² ≈ 0.39 vs. 0.0–0.32 for comparable baselines) and cross-modal tasks compared to prior models (significantly better in coding region segmentation, isoform retrieval, and  2-3 percentage points better in central dogma; measured in Acc, AUC, and F1).\n\nOverall Assessment\nCDBridge is a solid and biologically grounded contribution that advances cross-omics modeling by bridging DNA, RNA (as a latent bridge; not explicitly encoded) and protein representations through a context-aware, post-training framework. While the architectural novelty is moderate (stage 1 is based on ToMe/Token Merging; stage 2 is conditional transformer/standard cross attention; no new training objective or attention mechanism), the biological design insights and benchmark release make this work a meaningful step toward context-aware central dogma modeling. CDBridge’s novelty lies in the biological grounding of the architecture (splicing-inspired token merging, tissue-aware decoding), and the results show that the pretrained DNA and protein foundation models can be aligned post-hoc for expression prediction.", "questions": "1. How sensitive is performance to the merge ratio and to the pretrained model choices (e.g., DNA encoder backbone)? \n\n2. How does the GTEx-Benchmark differ statistically from existing Enformer/AlphaGenome datasets?\n\n3. How does the model handle unseen tissue embeddings (e.g., zero-shot tissue generalization)?\n\n4. Can the author comment on how does the model differ from the following related papers (not referenced in your work) \n4a. Garau-Luis, Juan Jose, et al. \"Multi-modal transfer learning between biological foundation models.\" Advances in Neural Information Processing Systems 37 (2024): 78431-78450.\n4b. Liu, Zicheng, et al. \"Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification.\" arXiv preprint arXiv:2502.07299 (2025).", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Biologically motivated architecture.\nThe splicing-inspired adaptive token merging and the explicit modeling of tissue context are well-designed and biologically meaningful. It nicely mimics exon selection and isoform reuse phenomena, showing awareness of biological grounding beyond standard transformer tricks.\n\n2. Strong empirical performance.\nThe reported R² and Spearman correlations across tissues are competitive and consistently improve upon strong baselines, suggesting that the cross-omics alignment and conditional decoding are genuinely effective. The cross-omics downstream tasks also show significant improvements, comparing to the baselines.\n\n3. Benchmark contribution.\nThe introduction of GTEx-Benchmark fills the gap in evaluating DNA-to-expression models, emphasizing isoform-aware, tissue-conditioned prediction. This benchmark could become valuable for future biological modeling studies.\n\n4. Practicality and scalability.\nThe framework leverages pretrained DNA and protein models without full retraining, making it computationally feasible and modular for extension to new modalities (e.g., RNA, metabolomics).", "weaknesses": "1. Evaluation design ambiguity.\nThe dataset construction process for GTEx-Benchmark could benefit from more details (e.g., splitting strategies, tissue balance, sequence selection). It’s unclear how much the benchmark is novel versus derived from existing GTEx/Enformer configurations.\n\n2. Ablation and interpretability is limited.\nThere is limited ablation showing the separate contributions of token merging, cross-omics alignment, and tissue conditioning. Moreover, while biological motivation is strong, interpretability analyses (e.g., learned exon attention, pathway enrichment) are missing.\n\n3. Comparison fairness.\nSome baselines (e.g., Isoformer, AlphaGenome) are not fine-tuned under the same supervision regime, and their task setups differ slightly. It is unclear if the performance gap reflects true model capability or evaluation setup differences.", "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": "5Xa8JA3nkY", "reviewer": "Reviewer_2RNv", "summary": "This paper presents CDBridge, a two-stage post-training strategy to model the \"central dogma of molecular biology\" by unifying pretrained DNA, protein, and RNA models. \n\nThe method consists of a token merging strategy to fuse DNA and protein representations to predict functional masks, as well as a tissue-conditional decoder for predicting cell-level protein expressions.\n\nImportantly, the authors introduce GTEx-Benchmark for the training and evaluation of tissue-aware central dogma modeling, which contains matched annotations of DNA, RNA, and protein.\n\nCDBridge is validated on gene-expression prediction for seen and unseen tissues, as well as its ability to segment coding regions of DNA, retrieve isoforms, and classify whether DNA-protein pairs are functionally associated.", "questions": "- How long were the DNA sequences and how many were used for evaluation?\n- How were the single-omics foundation models in Table 2 used to predict gene expression, considering that they \"lack explicit modeling of tissue-specific regulatory signals\"?\n- Why is the Enformer performance much worse than reported in the paper?\n- Can you compare to Borzoi (http://dx.doi.org/10.1038/s41588-024-02053-6), a successor to Enformer?\n- What is the performance of a linear model baseline for gene expression prediction?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The work is well-motivated and presents a new multi-omics model\n- CDBridge generalizes to unseen tissues for gene expression prediction\n- The figures describing the framework are well-made", "weaknesses": "The main weakness is the evaluation and details pertaining to it.  \n- It is unclear how the evaluation of gene expression prediction was performed\n- The single-omics foundation models in Table 2, as well as LucaOne, are too weak to be considered meaningful baselines.\n- The behavior in Figure 5, while interesting, is not extensively studied, in that the  reason \"non-coding regions are often merged\" may be that:\n  - almost all of the human genome is non-coding\n  - the embeddings for coding regions may be more information-rich compared to non-coding regions\n- Figure 6 shows selective token activation that is not strongly tissue-dependent, and therefore does not appear to reflect capacity for modeling tissue-aware gene expression.\n- In Table 4 (the ablation study):\n  - It appears that ignoring Stage 1 and only adding Stage 2 by itself saturates segmentation performance. \n  - The ablation study does not study whether there is a difference between the fixed and learned clustering in the presence of tissue clustering.", "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": "8OJL52fRe1", "reviewer": "Reviewer_DM1S", "summary": "This paper presents CDBridge, a novel post-training bridge strategy for context-aware biological modeling that addresses the critical challenge of mapping genomic DNA sequences to quantitative, tissue-specific expression levels. The proposed framework unifies pre-trained DNA and protein models through a two-stage approach: first, a sequence-context learning stage uses a splicing-inspired adaptive token merge to compress long genomic sequences into isoform-aware representations, and second, an environment-context learning stage employs a conditional decoder that integrates tissue embeddings to predict expression under diverse biological conditions. To rigorously benchmark this task, the authors introduce GTEx-Benchmark, a comprehensive dataset requiring models to resolve long-range exon dependencies, isoform reuse, and tissue-specific expression. Extensive experiments demonstrate that CDBridge consistently outperforms existing methods across both qualitative tasks like coding region segmentation and quantitative tissue-aware expression prediction, establishing a scalable and biologically faithful solution for cross-omics modeling.", "questions": "1. The adaptive token merging mechanism is a key component for handling long sequences. Could you provide more detail on how the merge ratio is sampled from a Gaussian distribution during training? Specifically, what were the chosen mean and variance, and what was the rationale behind this design choice over a fixed or learned ratio?\n\n2. The model demonstrates strong performance on unseen tissues, which is a significant result. Could you elaborate on the compositional nature of these \"unseen tissues\"? For instance, are they entirely distinct tissue types, or do they share cellular subtypes or regulatory programs with tissues seen during training? This would help clarify the extent of the model's generalization.\n\n3. In the ablation study (Table 4), the configuration with only Tissue Clust enabled showed a significant decrease in expression prediction performance. This could be due to insufficient gene embedding quality provided by scGPT or excessive information loss during pooling. Could the authors provide results using other single-cell foundation models or different pooling strategies?\n\n4. In Table 4, the segmentation performance (AUC and F1) is identical for the 4rd, 5th, and 6th rows. This seems to indicate that the segmentation task is solely determined by the frozen Stage 1 components. Could you clarify the configuration for the 5th row? It appears to show the same segmentation results without the key Stage 1 components (ToMe and Learned Clust), which is counter-intuitive.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper makes significant contributions to the field of computational biology by introducing a context-aware, post-training framework that effectively bridges the gap between DNA sequences and their functional protein outcomes. Its principal strengths are as follows:\n\n1. Novel and Biologically-Grounded Methodology: The proposed two-stage bridge strategy is both technically elegant and biologically inspired. The splicing-like adaptive token merging in Stage 1 efficiently handles the extreme sequence length disparity, while the conditional decoder in Stage 2 adeptly models tissue-specific regulation, directly addressing the central dogma's one-to-many mapping challenge.\n\n2. Strong and Comprehensive Empirical Validation: The authors provide compelling evidence for CDBridge's superiority through extensive experiments. It achieves state-of-the-art performance not only on the primary task of tissue-aware expression prediction but also on a suite of challenging downstream tasks including coding region segmentation, isoform retrieval, and DNA-protein association, demonstrating robust generalization to unseen tissues.\n\n3. Effective and Efficient Post-Training Strategy: The framework smartly leverages powerful, pre-trained single-omics foundation models (e.g., Evo2, ESM2) and aligns them with minimal additional parameters. This post-training approach makes advanced cross-omics modeling more accessible and scalable without the prohibitive cost of full end-to-end retraining.\n\n4. Enhanced Interpretability: The paper provides insightful analyses, such as visualizing the token merging process and tissue-aware activations, which show that the model's learned mechanisms align well with known biological principles (e.g., preserving exons, merging introns). This greatly enhances the model's transparency and trustworthiness.", "weaknesses": "While the paper presents a robust and compelling framework, there are minor limitations that could be addressed to further strengthen the work and its broader impact.\n\n1. Simplified Modeling of Biological Context: The current model conditions on tissue type as a categorical variable, which, while effective, is a relatively coarse-grained representation of a cell's state. This approach does not explicitly capture more dynamic or granular contextual factors known to influence gene expression, such as developmental stage, disease status, or specific microenvironmental cues. Extending the context modeling to incorporate such continuous or multi-factorial signals could enhance the model's biological fidelity and applicability to more complex physiological and pathological conditions.\n\n2. Dependence on High-Quality Annotations and Pre-trained Models: The performance of CDBridge is contingent upon the availability of high-quality, curated inputs, including precise isoform annotations from Ensembl and the pre-computed embeddings from large foundation models like Evo2 and scGPT. This reliance may limit the framework's generalizability to less-studied organisms, poorly annotated genes, or contexts where such powerful pre-trained models are unavailable or perform suboptimally, potentially restricting its utility in broader genomic applications.", "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)