BioBO: Biology-informed Bayesian Optimization for Perturbation Design
Authors: Yanke Li
Tianyu Cui
Tommaso Mansi
Mangal Prakash
Rui Liao
Venue: ICLR 2026 Poster (2026)
Keywords: Bayesian optimization; Biological priors; Perturbation design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Improve Bayesian optimization for perturbation design with biological priors.

Abstract: Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. 
BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. 
Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40\%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.

Published as a conference paper at ICLR 2026
BIOBO: BIOLOGY-INFORMED BAYESIAN OPTIMIZA- TION FOR PERTURBATION DESIGN
Yanke Li! Tianyu Cui', Tommaso Mansi', MMngal Prakash's | Rui Liao
"Johnson & Johnson Innovative Medicine, 2 ETH Zurich
ABSTRACT
Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specifie biological prior knowl- edge. We propose Giology-Informed Bayesian Optimization (BioBO), method that integrates Bayesian optimization with multimodal gene embeddings and en- richment analysis, a widely used tool for gene prioritization in biology, to en- hance surrogate modeling and acquisition strategies. BioBO combines biologi- cally grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40%, and consistently outperforms conventional BO by identifying top-performing pertur- bations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.
INTRODUCTION
In vitro cellular experimentation with genomic interventions is critical step in early-stage drug dis- covery and target prioritization. By perturbing genes and observing cellular responses, researchers infer gene function and identify potential therapeutic targets (Chan et al.) 2022 Bock et al., 2022). Techniques such as CRISPR-Cas9 (Jinck et al.) 2012 Jiang & Doudnal 2017 knockout screens enable systematic perturbation of individual genes, but they are often resource-intensinn and coneeconsuming. Given the vast number of protein-coding genes in the human genome (approxi- mately 20,000), exhaustively testing all possible perturbations infeasible (Abascal et al., 2018)) Consequently, strategies that efficiently select the most informative experiments are essential to ac- celerate drug discovery while minimizing experimental costs.
Bayesian experimental design provides a principled framework for this challenge. In particular, Bayesian optimization (BO) offers a sample-efficient approach to identify genes whose perturba- tion maximizes desired cellular phenotypes. BO relies a probabilistic surrogate model, such as Gaussian process (Williams& & Rasmussen 2006) or Bayesian neural network (Springenberg et al. 2016), to model the response surface, and acquisition function to balance exploration of uncer- tain regions with exploitation of promising candidates (Frazier 2018). While recent works have applied BO gene perturbation design (Mehrjou al.) 2022 Lyle al. 2023). they typically use generic, uni-modal gene representations (or embeddings) and do not fully leverage rich biological knowledge. limiting their performance. Integrating multimodal gene representations, which capture sequence, functional, and network- based information, can provide more informative representations and improve the efficiency of experimental selection.
Beyond richer gene representations. explicit biological priors can further guide experimental de- sign. For example, gene set enrichment analysis (EA) identifies pathways that are statistically over-
"These authors contributed equally as first authors. Conreponnencee to: ccui80its com
These authors contributed equally as senior (last) authors.

---

Published as a conference paper at ICLR 2026
Our lorthooooooogcal contributions
Enrichmant analysis prior
perturbations phrnnstpes
Labeled
Modality fusion
Acquisition unlabeled
perturbations
Gendiie
CaneT
MADD
Figure/ I: BioBO pipeline for perturbation design. We make two methodological innovations: (i). Fusion of gene modalities to improve surrogate modeling: (ii). Enrichment analysis on top of surrogate model predictions to strengthen gene acquisition via incorporating biological information.
represented among the hop-performing genes, providing information on molecular mechanisms and potential high-value targets (Subramanian et al. (2005). However, conventional EA has two key lim- itations: (i) it lacks granularity, treating all genes within a pathway as equally promising, and (ii) it is purely exploitative, potentially biasing experiments toward well-characterized pathways while neglecting unexplored regions of the genome.
To address these limitations, we propose Biology-Informed Bayesian Optimization (BioBO), framework that integrates multimodal gene representations and biological priors, such as enrich- ment analysis (Figure D.. into BO. BioBO helps balance exploration and exploitation, efficiently guiding experiments toward both well-characterized and underexplored genes. Together, these ad- vances make BioBO framework for efficient, interpretable, and effective experimental design, accelerating targeted discovery in genomic perturbation studies. Our key contributions are as fol- lows.
1. We introduce multimodal gene embeddings, integrating multiple sources of biological in- formation in the surrogate modeling to improve the designs of BO.
2. We demonstrate that the improvement of BO from multimodal embeddings mainly from the improvement of surrogate model on regimes close to the optimum rather than on the entire data distribution.
3. We augment the acquisition function in BO using enrichment analysis within the theoreti- cally principled n-BO (Hvarfner et al. (0022) framework. This approach incorporates prior biological knowledge while maintaining principled ppploation-eeploitatiioo trade-off and provides interpretable insights into experimental design.
4. We empirically validate BioBO on established public benchmarks, showing that it outper- forms conventional BO improves labeling efficiency by 25 25-40%. and identifies biologically coherent pathways with markedly stronger enrichment signals.
2 BACKGROUND AND NOTATION
2.1 NOTATION AND PROBLEM SETUP
We consider the task of optimizing a black-box function f G -> R, which maps each gene g E G represented by the set of integers or one-hot embeddings to value f(g) E R denoting the change of cell phenotype under the gene knockout, across the entire finite gene space with |G| z 20, 000 (i.e., the number of protein-coding genes in human). Similar to (Lyle et al.) 2023), we use biologically informed d-dimensional embeddings of genes, X G + X, which maps each X C Rd capturing the biological relationships with other genes. Moreover, the gene embeddings X construct a one-to-one mapping from G and contain the same number of distinct d-dimensional vectors G, i.e., [ |G], use f(x) and fg) interchangeably where æ is the embedding of the gene g. Therefore, we define the optimization problem as follows
gene y E G corresponding d-dimensional vector X (g))
x * E arg max f(x). ØEX
(1)
2

---

Published as a conference paper at ICLR 2026
In practice, ((x) is expensive to evaluate because requires a CRISPR-Cas' knockout experiment in the lab, and we would like to maximize [()) in an efficient manner by only evaluating a small number of points from X. For this work, we do not perform wet-lab experiments ourselves; instead, we simulate the online BO loop by querying from pool of genes with pre-measured phenotypes, as is standard practice in BO and Active Learning (AL.) studies (Filstroff et al. 2021; Gupta et al.) 2021 Li et al. 2024). While in practice, BO would operate on truly unlabeled genes, retrospective evaluation on fully labeled datasets necessary to quantify and showcase the benefits of any BO or AL method.
2.2 BAYESIAN OPTIMIZATION
Bayesian optimization (BO) (Mockus 1998: Frazier 2018 is model-based black-box function
optimizer that employs a probabilistic model, e.g., Gaussian process (GP) (Williams & Rasmussen, 2006) or Bayesian neural network (BNN) (Springenberg et al., 2016), as a surrogate model. Specif- ically, BO optimizes J' from an initial experimental design D, = {(x,, J[) and sequentially deciding on one or batch (with size B) of new designs label and form the data Dn+1 D, B, with new labeled dataset B, = {L Vn.,)) )= for the n-th iteration with n E (,,...,,N) .N.. At each iteration 71, BO learns a probabilistic surrogate model fn p fn (P,)) approximate the true function f. where p(fn, (D,)) is the posterior distribution of a GP or BNN given the labeled data. Us- ing the predictive uncertainty from ( BO selects next designs by optimizing an acquisition function (AF), Op(fxI,, (z), across the set of unlabeled data points.
Acquisition functions encapsulate the underlying utilities; therefore, they correspond to the trade-oft off between exploitation (using the current optimum from the surrogate model) and exploration (consid- ering the uncertainty of the surrogate model). Popular choices of AF include Expected Improvement (EI) (Jones et al., 1998) and Upper Confidence Bound (UCB) (Srinivas et al. 2010). For instance, El selects the next point x that maximizes the expected improvement:
a BI
().,Ip.)) B1]f.(x)) - wM = Zo, (z)))()(2)) + (z)))))
where v is the best outcome observed so far, Z fa())) (ze) with pnn (x) and on (ax representing (m)
the mean and variance of the posterior p( (Pn) respectively, and ø(-) and ((-) are the PDF and CDF of standard Gaussian distribution. UCB is detined as:
UCB Op p.,) (..) (x) + (x),,()),
(3)
where Kn, is the user-specified parameter controlling the exploration exploration-ecploitatinn trade-off. Both El and UCB provide myopic strategy for determining informative designs with theoretical guaran- tees (Bull 2011 Srinivas et al., 2010). Other popular myopic acquisition functions include Prob- ability of Improvement (PI) (Jones] (001), Thompson Sampling (TS) Thompson] 1933), and Dis- coBAX (Lyle et al., 2023). In this work, we mainly focus on using BNNs as surrogate models and UCB, EI, TS, and DiscoBAX as acquisition functions, similar existing works on perturbation design (Mehrjou et al. 2021 Lyle et al. 2023); however, our work applies to other probabilistic models and myopic acquisition functions well.
2.3 ENRICHMENT ANALYSIS
Enrichment analysis (EA) or over-representatioon analysis is a computational approach used to de- termine whether a set of genes associated with a specific biological process or pathway appears more often than expected by chance (Boyle et al,, [2044] Khatri et al. 2012, Huang et al. 2009). Specifically, given background gene set, c.g., all protein-coding human genes G, and subset S C G of genes of interest, EA tests whether a pathway i, i.c., a predefined gene set PP C G. with known biological function provided in pathway databases, such as Hallmark (Liberzon al. 2015 is represented in S staristically more frequently than expected by chance.
EA has been widely used to design experiments in applications such target prioritization and biomarker expansion (Katz et al.) 2021 Zhao et al.] [2022; Dai et al.] 2022, Ramos et al., [023] Ordóñez al. 2024). Intuitively, if several desirable genes have been identified, EA can be applied
3

---

Published as a conference paper at ICLR 2026
to discover the pathways enriched by those desirable genes. Therefore, other untested genes in those significantly enriched pathways would construct a good candidate set for the next round of experiments. The significantly enriched pathways serve as biologically informed prioritization framework for designing experiments, allowing us to target molecular processes where the desirable genes are most likely be. This approach ensures that experimental interventions are focused on high-value genes within the biological network, thereby increasing the likelihood of eliciting interpretable system-level responses while reducing experimental redundancy.
Although EA serves as well-established, biologically informed experimental design framework, contains two major shortcomings:
1. Lack of granularity: EA can prioritize pathways; however, all untested genes in the same pathway are equally likely. This can still construct a huge pool if the significantly enriched pathway is large.
2. Lack of exploration: EA-based experimental design is pure exploitation process and has po- tential bias toward known biology. The significantly enriched pathway would be more exploited by selecting more genes from it, and sinnsignnf pathways will never be explored.
In this work, we propose a principled approach to combine the BO-based and EA-based experimen- tal design framework to equip BÔ with extensive domain information in biology from EA and equip EA with granularity and exploration from BO.
3 METHOD: BIOLOGY-INFORMED BAYESIAN OPTIMIZATION
3.1 SURROGATE MODELLING WITH MULTIMODAL GENE REPRESENTATIONS
We first improve BO by improving the surrogate modeling. Specifically, we propose to use multi- modal gene embeddings rather than the uni-modal embeddings used in the existing gene perturbation design literature (Mehrjou et al. 2021 Lyle et al., (023). We consider the following two extra gene
embeddings that are effective in many geve- tasks (Yang et al. 2022 Chen & Zou 2025):
Gene2Vec Duu et al. ontology (Ashburner et al.
2019),
2 gene embeddings encode gene-gene relations defined in gene
2000 learned with self-supprvised learning;
GenePT Chen & Zou
2025 a GenePT ChatGPT embeddings of genes based on the literature.
We use Bayesian Neural Networks (BNNs) as surrogate models similar to previous works (Mehrjou jet al. 2021 Lyle et al. 2023), and we concatenate the original gene embedding x with the gene embeddings from the above-mentioned modalities as the input of BNN, i.e., f((xr, .e x GeGneeT)). We also explore latent-space fusion strategy, which learns joint representation integrating the heterogencous biological modalities in latent space either via concatenation or using cross-attention.
In Section 4.3 we design a comprehensive analysis to study relations between the performance of BO and surrogate models to reveal reasons behind the benefits of multimodal fusion in BO settings.
3.2 AUGMENTED ACQUISITION FUNCTION WITH ENRICHMENT ANALYSIS
Vanilla BO ignores prior beliefs about the optimum's 's location, overlooking valuable knowledge that could enhance the search. We mainly focus on BO (Hvarfner et al., 2022), a principled general- ization of the acquisition function to incorporate prior beliefs about the location of the optimum in the form of probability distributions π(æ). Specifically, for acquisition function ap((aID.) (x), the corresponding augmented acquisition function is:
TQP(F.IP..)(z)) ap(s, (..). (p.s..
(Z)
where 3 is a hyperparameter set by the user (see sensitivity analysis of 3 in Appendix C reflecting their confidence in n. (x), and Ln is the number of labeled data so far. This reflects the intuition that, as the optimization progresses, BO will likely have enough data to reach the optimum confidently. This also comes with theoretical properties described in the next section.
should increasingly trust the surrogate model over the prior, as
In this work, we propose to augment the acquisition function with the prioritization results from enrichment analysis prior within the rBO framework. Enrichment analysis comes with statis-
4

---

Published as a conference paper at ICLR 2026
tical hypothesis tests: under the null hypothesis Hu. that genes in 8 are sampled uniformly from G, the probability of observing at least [ n PP// overlaps follows the upper tail of the hypergeometric distribution; therefore, we can compute the p-value with
min((P.1.18))
P
PP: - (
G
G S
p(P.)
Σ SUP
(5)
and multiple hypothesis testing across all pathways is controlled via Bonferroni correction (Haynes) 2013) to derive the adjusted p-value, p"P( One can also compute the odds ratio, o(P,). .) from the EA results by constructing the contingency table, and high oP,.) (e.g.. o(P,) > 1) indicates that P, is over-represented in $ compared to random. Chen et al. (013)) propose to combine the p-value and odds ratio to evaluate the overall representativeness with c(IP,) (TP, log p(P:), which will be used to design the biologically informed prior n, (2) at each iteration.
At each iteration n, we rank labeled genes according to their labels (i.e., change of phenotype under the gene knockout). We consider the top-k (we use top-10% in this paper and report the results with di) genes as the genes of interest, i.e., S,, and enrichment analysis (Chen al. (013) find top enriched pathways, ranked by the combined score c(P,). We additionally provide sensitivity analysis of BioBO this choice of k in Appendix H If one unlabeled gene is within the top pathway, we
increase the probability of selecting the gene in the acquisition function. Specifically, we define the probability of selecting an unlabeled gene x: as follows:
(x)(
Sn (x) = logit(
agg (
[ea (P:)), (xx) Σn (*)(m))
(6)
Un
where U,, is the number of unlabeled genes at iteration n and age[-] is set aggregation operation that summarizes the combined score (-) at iteration n across all significant pathways (with adjusted p-value pm (IP+) < 0.05) that contains the unlabeled gene æ. We use mean operation in the paper to measure the averaged representativeness in all significant pathways. We also explore the max operation in Appendix C which shows benefits as well. The hyperparameter temperature controls the level of information that keep from the enrichment analysis. When t oc, x (2x) reduces to a uniform distribution, and EA will be ignored. We use 1 = 0.1 in all experiments.
3.2.1 THEORETICAL PROPERTIES
BioBO comes with the same no-harm guarantee as the original BO (Hvarfner et al. 2022), because of the decaying effect of the prior in Eq when employed with myopic AFs (all AFs used in this paper). For instance, when paired with the EI, we can prove that the regret, C,, (BioEI,), to the optimum at iteration of the BioEI strategy, i..,, using EI in Eq 4 the corresponding El strategy, C, (EI,,, using the Theorem 1 of Hvarfner et al. (2022) as following:
be bounded by the regret of
|maxæ TT (xx
L. (BioEI,)) & CzIn Cr,n
ming JT, xT.
For detailed conditions and proofs of the above Theorem, please refer to the original BBO paper (Hvariner al.] (022). Therefore, we have the no-harm guarantee that the regret of the BioEI strategy is asympiotically equal to the regret of the El strategy:
(BioEI,,) L, (EI,),
which indicates that BioEl is robust against errors and biases from the enrichment analysis.
4
EXPERIMENTS
4.1
GENEDISCO DATASETS
Datasets We use five genome-widd CRISPR assays from the GeneDisco dataset (Mehrjou et al. 2021) and present the analysis for the two most widely-used datasets from literature (IFN-y and IL-2) in the main text, while the same analysis for others shown in Appendix. We use the Achilles gene descriptor, i.e., gene embedding X, from GeneDisco. Although GeneDisco includes other two
5

---

Published as a conference paper at ICLR 2026
0:38
0.78
6.24
6.22
0:50
0.64
0:22
0.0
Cydls
Cyc
Cycles
Cyc 50
Acqusition Cycles
Figure 2: Performance across single modalities (Achilles, Gene2 Vec, GenePT) and their Fusion on IFN-y (top) and IL-2 (bottom). Row-wise dashed lines indicate the Fusion value the final cycle (20) for UCB, EI. TS, and DiscoBAX to aid comparison. We observe that BO with Fusion is better than BO with any single modality.
gene descriptors, CCLE and STRING, only Achilles is informative to predict the cell phenotypes, as shown (Mehrjou al. 2021 Lyle et al., 2023) and Appendix Section [. therefore, we focus on Achilles from GeneDisco. For richer gene representations, include two additional embeddings: Gene2Vec and GenePT (in Section 3.1) to leverage multimodal genetic descriptors. For additional details on datasets and descriptors, see Appendix Section
go beyond unimodal Achilles and
Measure the performance of BO We Cumulative Top-k Recall to measure the ability of method to identify the top gene perturbations as those in the top percentile of the experimentally measured phenotypes following Lyle et al. (2023).
Measure the performance of surrogate models We evaluate surrogate models separate test set using LL (log-likelihood) for the quality of predictive distribution and RMSE (Root Mean Squared Error) for the prediction accuracy. Moreover, caleulate LL and RMSE subsets of the test data that are close to the optimum, e.g., LL@top-100% represents the LL on the test data points whose labels are within the top 10%, to evaluate the model performance near the maximum.
Baselines For surrogate models, we use BNN in Lyle et al. 2023). using Achilles, Gene2Vec, GenePT, and Fusion (i.e., the fusion of three modalities). We use UCB, EI, TS, DiscoBAX as acquisition functions, as well as augmented acquisition functions, BioUCB, BioEl, and BioTS, with biological priors from enrichment analysis using Gene Ontology (GO) (Ashburner et al. (000) and Hallmark (HM) [Lberzzon et al. 2015) databases. We run each experiment with different seeds.
4.2 EXPLORING EFFECTS OF USING MULTIMODAL GENE REPRESENTATION IN BO
First, we study the effects of using multimodal gene representations, i.c., the Fusion, in surrogate models. Figure 2 shows the cumulative top-k recall of different acquisition functions each cycle of the experimental design. We observe that all BO acquisition functions are better than random, es- pecially UCB, and BO saves the labeling efforts 25% 75% compared with random, which indicates the benefits of BO in experimental design. Moreover, observe that using surrogate models with the Fusion always better than using single-modal surrogate models, with labeling effort saving ranging from 4% to 40%. The best-performing model is using the Fusion with UCB. We also ob- serve that DiscoBAX Lyle et al. 2023) worse than existing standard acquisition functions") and hence remove DiS in the subsequent experiments. In addition, as detailed in Appendix
'This observation is consistent with issue reported by the DiscoBAX authors in their official GitHub repository (Issue #3), noting that the originally reported performance affected by an implementation bug.

---

Published as a conference paper at ICLR 2026
Achitas Gensime
Feesh Toais Aeralive Labto SX Sproman hoarson 6.16
Fleys Tre keent well Gecal
Tipe Recd oe game Speomen 6.3 Aaatoon 6.12
Trais kocalize llebre
Spasman it 22, hearion of
Speoman de harton
kp-k claual Sposmon i VL Aearam
IL2 Speamon d.6 Raarinn 6.6)
Spramon ii, Jaaron 0.46
Spromen io... learow
-1.00
0.01
030 111 621 car EIE E32 122
221 E3C 11c OTE SIB o2
221 D30 112 aa G12 oi2
221 Bir Eir cit Gás OR
Figure 3: Relations between performance of BO and the surrogate model. We observe that Fusion (red) does not improve the surrogate model globally (LL global, first column). However, it improves on data points that are near optimum (LL@top-1% to (.@ttop-10%) which explains the improvement on BO results (top-k recall). Specifically, the top-k recall of BO is more correlated with local LL than global LL, measured by both Spearman and Pearson correlation.
The latent-space fusion strategies further improve BO performance over simple concatenation-basee fusion, highlighting the advantage of integrating heterogeneous modalities more effectively.
4.3 ANALYZING RELATIONS BETWEEN PERFORMANCE OF BO AND SURROGATE MODELS
Observing the benefits of Fusion in BO from Figure 2 we further analyze why using Fusion in the surrogate model improves BO. One intuitive hypothesis is that: a more expressive multimodal gene representation improves the predictive distribution of the surrogate model, which leads to better Bayesian Optimization. We test this hypothesis by estimating the correlation between the perfor- mance of BO and surrogate model. Specifically, we divide the dataset into training and testing: we run BO loops on the training set and measure the performance of BO (cumulative top-k recall), and measure the performance of the surrogate model on the test set. We plot the performance of BO (cumulative top-k recall) and the surrogate model (test LL) in Figure
We find that the correlation between cumulative top-k recall and LL is negative (first column in Fig- ure 3). meaning a higher LL does not lead to a better BO. Although counterintuitive, it is consistent with the conclusions from Foldager et al. 2023). In BO, however, the surrogate is primarily used to estimate the relative ordering of high-value candidates and to locate the local optimum near top- performing genes, rather than achieve high global predictive accuracy. As also noted in Foldager et al. (202)), global likelihood therefore has limited influence on the acquisition function. Thus, even if fusion does not improve global likelihood, it can still enhance BO performance when it sharpens the surrogate locally. We observe precisely this effect: the predictive distribution of the surrogate model improves near optimum (red dots are higher than others on average: second, third, and fourth columns in Figure 33 which is positively correlated with the BO performance significantly, with Spearman correlation ranging from 0.31 to 0.49 for IFN-y and being around 0.64 for IL-2. Moreover, we observe the highest Spearman correlation of cumulative top-k recall is with LL @L@@@ top- 1% on 4/5 datasets (see the results for other three datasets in Appendix Section E.3). Therefore, conclude that: multimodal gene embedding improves the predictive distribution of the surrogate model near optimum, which leads to a better Bayesian optimization.
4.4 EXPLORING EFFECTS OF COMBINING ENRICHMENT ANALYSIS IN BO
Here, we study the benefits of combining enrichment analysis with BO using the proposed BioBO framework in design experiments. First, we analyze if the prior distribution in Eq[6] constructed from results of enrichment analysis, is beneficial in experimental design, i.e., using model-free approach. We select genes with the highest prior probabilities (greedy selection) in Eq Figure 4(a) shows that using both Gene Ontology and Hallmark the pathway database for enrichment can improve the design compared with random selection of genes, demonstrating the potential of enrichment analysis to inform experimental design. However, this approach is purely exploitative.
7

---

Published as a conference paper at ICLR 2026
Pure EA
BiouCB Astilles
Random
E4-G0 Greedy
E4-M Greedy
UCB
BOUCC
BHIUC HM
Rondom
Pure FA: IFN-V
Pure EA: IL-2
Biot/C on Achilles: IFW-y
BisUCC on Achilles: IL-2
Gumulabue Topa Rocal!
6.14
Cuma mwwabee Top-k Rocall
0.4
0.2
0.12
020
0.08
0.38
6.1
0.24
0.22
0.20
Cycles
Cycls
Cycles
Cycles
Figure 4: Performance of pure EA and BioUCB on Achilles. (a): Pure EA on IFN-y and IL-2. We observe that pure EA provides better designs than random. (b): BioUCB on Achilles for IFN-q and 21.2. We observe that BioUCB provides better designs than UCB and pure EA.
Table I: Cumulative top-k recall with standard error of each acquisition function on different datasets. We observe that BioBO achieves the best performance on 23/24 different settings, and BioUCB- with surrogate function using fused features achieves the best performance for both IFN-y and IL-2. The best performance (with the smallest standard error) is bold.
Phenotype: IFN-y
Fusion
Achilles
GenePT
Gene2
EI BioEI-GO (ours)
0.093 (0.001)
0.072 (0.001)
0.077 (0.004)
0.071 (0.006)
0.098 (0.000)
0.085 (0.000)
0.095 (0.005)
0.079 (0.004)
BioEI-HM (ours)
0.096 (0.001)
0.076 (0.001)
0.096 (0.007)
0.079 (0.002)
TS
0.083 (0.001)
0.068 (0.001)
0.088 (0.002)
0.073 (0.002)
BioTS-GO (ours)
0.095 (0.001)
0.073 (0.000)
0.097 (0.004)
0.095 (0.005)
BioTS-HM (ours)
0.097 (0.001)
0.097 (0.005)
0.093 (0.005)
0.081 (0.004)
UCB
0.100
(0.000) (0.001)
0.077 (0.001)
0.086 (0.004)
0.093 (0.005)
BioUCB-GO (ours)
0.102 (0.001)
0.098 (0.002)
0.092 (0.005)
0.098 (0.002)
BiOUBB-HM (ours)
0.109 (0.001)
0.085 (0.003)
0.101 (0.001)
0.103 (0.004)
Random
0.050 (0.001)
0.050 (0.001)
0.050 (0.001)
0.050 (0.001)
Phenotype: IL-2
Fusion
Achilles
GenePT
Gene2
0,148 (0.002)
0.130 (0.003)
0.107 (0.005)
0.109 (0.002)
BioEI-GO (ours)
0.147 (0.003)
0.138 (0.003)
0.107 (0.005)
0.115 (0.002)
BioEI-HM (ours)
0.153 (0.002)
0.130 (0.003)
0.107 (0.005)
0.109 (0.002)
TS
0.142 (0.001)
0.119 (0.001)
0.113 (0.014)
0.113 (0.002)
BioTS-GO (ours)
0.147 (0.003)
0.142 (0.002)
0.119 (0.011)
0.119 (0.001)
BioTS-HM (ours)
0.153 (0.002)
0.123 (0.004)
0.139 (0.013)
0.124 (0.002)
UCB
0.174 (0.001)
0.143 (0.003)
0.118 (0.01I)
0.123 (0.000)
BioUCB-GE (ours)
0.169 (0.001)
0.158 (0.001)
0.131 (0.008)
0.133 (0.002)
BioUCB-HM (ours)
0.178 (0.001)
0.163 (0.001)
0.138 (0.012)
0.127 (0.000)
Random
0.049 (0.001)
0.048 (0.001)
0.049 (0.001)
0.046 (0.002)
Next, we combine the enrichment analysis prior with the acquisition function in BO, i.c., the model- based BioBO approach, thus balancing iloiitttoon-xpllraiiiio trade-off explained in Section 3.2 We observe that adding the enrichment analysis prior can improve the labeling efficiency over BO with the corresponding acquisition function without the prior. Specifically, the enrichment analysis prior improves the labeling efficiency of UCB by 20% with Achilles gene embedding on optimizing IFN-y. We show the cumulative top-k recall of all experiments in Table [2] where observe that the prior from enrichment analysis can improve the original acquisition function most of the time (23/24 cases). The best performance is achieved by BioUCB using the Hallmark database for building the enrichment prior with fused gene embeddings in both IFN- and II.2.
4.5 INTERPRETABILITY OF DESIGNS
In this section, we conduct enrichment analysis using the Hallmark dataset to provide biological interpretations of selected genes by BO. We compare models the IFN-y dataset: the baseline
8

---

Published as a conference paper at ICLR 2026
Table Enrichment analysis results of designs from existing method and BioBO. We observed that our BioUCB-HM with multimodal gene embedding shows significantly stronger enrichment signals compared to existing approach (BO with UCB).
Phenotype: IFN-y; Feature: Achilles; Acquisition: UCB
Pathway
Overlap
Adjusted p-value
Odds Ratio
Combined Score
MYC TARGETS V1
32/200
2.71 x 10-13
7.25
237 .22
B2F_TARGO TARGETS
22/200
5.10 x 10
4.32
66,18
DNA
14/150
3.87 x 10-3
3.45
28.51
G2M CHECKPOIN
15/200
1.79 10-2
2.7
17.42
Phenotype:
IFN-3; Feature: Fusion; Acquisition: BioUCB-HI
Pathway
Overlap
Adjusted p-value
Odds Ratio
Combined Score
VYC.TARGETS.V1
187/200
4.98 x 10 247
766.31
4.37 x 10
E2F TARGETS
48/200
1.07 x 10-16
5.92
235.98
40/200
3.93 x 10
4.52
120.41
TYC.TARGETS_V2 18/58
2.90 x 10-8
7.66
151.48
UCB + Achilles and our method BioUCB-HM + Fusion. Table 2 shows that BioUCB-HM pro- duces markedly stronger enrichment signals in pathways closely tied to IFN-y regulation in T cells. While UCB identifies relevant pathways such as
and E2F _.FRGATARTTS with mod- est overlaps (32/200 and 22/ 2200) and adjusted p-values in the range of 10 to 10-2 - BioUCB-HN shows stronger enrichment signals compared to UCB. For example, MYC TARGETS V1 reaches an extraordinary overlap of 187/200 genes with an adjusted p-value of 4.98 10-217, yielding combined score over 1,000-fold higher than UCB. Similarly, other critical pathways such as E2F_TARGETS and 322MCHECKPOINT not only remain significant but also demonstrate substan- tially higher overlaps and more robust statisties under BioUCB-HM while BioUCB-HM further uncovers CYC..ARGETS. V2, missed entirely by UCB. From a biological perspective, these path- ways central regulators of cell growth, proliferation, and metabolism. MYC drives effector T cell proliferation but can restrain differentiation, so its inhibition is consistent with enhanced IFN-y production (Meinik et al. 2019). Likewise, targeting E2F and G2M checkpoint regulators reduces proliferation pressure and shifts T cell programming toward cytokine output, while DNA repair mechanisms also intersect with stress responses in activated T cells (Ren al. 2002). The observa- tion that knockout of genes in these pathways increases IFN-y log fold change supports the idea that restraining proliferative and metabolic circuits frees cells to mount stronger effector responses. Thus, BioUCB-HM not only outperforms UCB quantitatively but also pinpoints biologically mean- ingful regulatory axes MYC, E2F, and G2M G2M-that that provide a mechanistic rationale for boosting IFN-y production in T cells. Further analysis of underexplored biologically novel genes prioritized by BioBO is detailed in Appendix N alongside the biological mechanistic interpretability of these genes.
Beyond IFN-7 we additionally evaluate BioBO on a second immune-cell perturbation IL-2 dataset and observe qualitatively similar interpretability gains: BioUCB-HM consistently produces markedly stronger and more biologically coherent enrichment signals than baseline UCB. Full re- sults and pathway-level statistics provided in Appendix!! [..
4.6 COMPUTATIONAL EFFICIENCY OF BIOBO
Runtime per iteration of BioBO is comparable to existing BO methods. We report detailed runtimes in Appendix G The choice of 20 acquisition cycles (selecting 400 genes with 20 genes per cycle) follows exactly the experimental protocol established in (Mehrjou et al. 2021 Lyle et al. 2023), ensuring comparability. The total of 400 perturbations selected by 20 iterations corresponds to less than 5% of the typical gene pool, aligning with realistic experimental budgets in high- thotuhutu CRISPR screens (Mehrjou et al. 2021: Lyle et al. 2023). Thus. BioBO maintains its fastness from a practical standpoint and identifies high-value perturbations more efficiently compared to baseline methods.

---

Published as a conference paper at ICLR 2026
5 OTHER RELATED WORKS
Exploiting external knowledge for drug discovery Incorporating external knowledge has re- cently been studied extensively in drug discovery. Neural network architectures that have domain- specific inductive biases (Cui et al. 2022a Yazdani-lahromi et al. 2025 Moskalev et al. (0225) and
explicit Bayesian priors constructed from existing knowledge (Cui al. 2022b Skok Gibbs al.
2024
Cui et al.) 2025 de la Fuente Cedeño et al.) (025) have been proposed for various purposes.
In BO, external knowledge can be elicited from the feedback of human experts through preference
learning and used in BO (Mikkola et al. 2020; Adachi et al.) (023) when the explicit knowledge is challenging to obtain. However, when the external knowledge on the input space over the potential
candidates is ready, it can be either treated constraint tHernández-Lobat et al. 2015 Adachi
ict al. 2022) or a prior belief (Souza et al. 2021 BioBO fits within this framework.
Hvarfner et al. 2022 Cissé et al. 2024 and our
Experimental design in drug discovery Many drug discovery and design applications use exper- imental design to speed up the process. Active learning, a framework that finds the most informative unlabeled datapoints to label for improving the model, has been applied to molecular property pre- diction (Neporozhni et al. 2025 Masood et al. 2025). Perturb-seq experiments (Zhang et al.) 2023 Huang et al. 2024). and genomics CRISPR assays (Mehrjou et al., (02T). Active learning uses the information gain of the probabilistic surrogate model to guide the selection, such as BALD (Houlsby et al. (011)) and EPIG (Smith et al. 2023); therefore, it is an exploration-only process. On the other hand, BO trades off between exploration and exploitation to query the most informa- tive unlabeled datapoints the optimum. BO has been applied bio-sequence optimization by combining with deep generative models, including small-molecular and protein sequences (Gómez- Bombarelli al.) 20188 (Stanton et al.) [202 Gruver al.
2023 Ramchandran al. 2025), as
well as on genomics CRISPR assays (Pacchiano et al. [003 Lyle et al.] (023).. Recently, large language model (LLM) based agents have shown potential in experimental design by leveraging
the rich background knowledge and reasoning capabilities (Lee et al., 2024] Roohani et al.) 2025))
and enrichment analysis has been shown to an important tool in the multi-agent system (Hao et al. 2025). Different from heuristic designs with LLM, we focus on well-principled Bayesian experimental design framework.
CONCLUSION
We introduce BioBO, a biology-informed BO framework for perturbation design, combining stan- dard BO with multimodal gene representations and enrichment analysis to guide experimental pri- oritization. Our theoretical analysis establishes a no-harm guarantee when integrating biological priors from enrichment analysis, ensuring robustness to noisy biased pathway information. Em- pirical results on the GeneDisco datasets demonstrate substantial gains in sample efficiency, with BioBO outperforming traditional BO methods and enrichment-only strategies. By fusing princi- pled optimization with domain-specific biological insights, BioBO enables more efficient discovery of high-value perturbations, reducing experimental costs. We also analyze failure cases, showing that when an incorrect or biologically mismatched pathway resource is used, the enrichment prior becomes uninformative and BioBO gracefully reduces the underlying surrogate model (see Ap- pendix[]] Finally, we evaluate BioBO in realistic settings where some embedding modalities are un- available, showing that simple KNN-imputation preserves strong performance and that multimodal fusion continues to outperform single-modality surrogates (Appendix [). BioBO can also integrate multiple enrichment sources simultaneously, and ensemble priors consistently match or outperform individual databases (Appendix M.. Looking forward, this approach provides foundation for in- tegrating broader biological knowledge sources-ste such as single-cell profiles and l embeddings into experimental design frameworks, paving the way for faster and more targeted advances in genomics and therapeutic discovery.
10

---

Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we provide data description in Section A and implementation details, including choice of computational platform and model hyperparameters, in Section B Code will be released upon acceptance.
REFERENCES
Federico Abascal, David Juan, Irwin Jungreis, Laura Martinez, Maria Rigau, Jose Manuel Ro- driguez, Jesus Vazquez, and Michael Tress. Loose ends: almost one five human genes still have unresolved coding status. Nucleic acids research, 46 46 (6(14):7070-7084 2018.
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, and Michael A Osborne. Fast bayesian inference with batch bayesian quadrature via kernel recombination. Advances in Neural Information Processing Systems, 35: 35:16333-1654 2022.
Masaki Adachi, Brady Planden, David A Howey, Michael A Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, and Siu Lun Chau. Looping in the human collaborative and explainable bayesian optimization. arXiv preprint arXiv:2310.17273, 2023.
Abdalla Akef, Kathy McGraw, Steven D Cappell, and Daniel R Larson. Ribosome biogenesis is downstream effector of the oncogenic u2afl-s33 mutation. PLoS biology, :8(10)2:,, 2020.
Michael Ashburner, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, Allan P Davis, Kara Dolinski, Selina Dwight, Janan Eppig, et al. Gene ontology: tool for the unification of biology. Nature genetics, 25(1):25- (((1):25-22))) 29, 2000.
Christoph Bock, Paul Datlinger, Florence Chardon, Matthew A Coelho, Matthew Dong, Keith Lawson, Tian Lu, Laetitia Maroc, Thomas M Norman, Biena Song, et al. High-content crispr screening. Nature Reviews Methods Primers, 2(1):8, 2022.
Elizabeth Boyle, Shuai Weng, Jeremy Gollub, Heng Jin, David Botstein, Michael Cherry, and Gavin Sherlock. Go:: Termfinder- open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinfor- matics, 20(18):370-33715, 2004.
Adam Bull. Convergence rates of efficient global optimization algorithms. Journal of Machine Learning Research, 12(10), 2011.
Yau-Tuen Chan, Yuanjun Lu, Junyu Wu, Cheng Zhang, Hor- Yue Tan, Zhao-xiang Bian, Ning Wang, and Yibin Feng. Crispr-cas9 library screening approach for anti-cancer drug discovery: overview and perspectives. Theranostics, 12(7):3329, 2022.
Edward Y Chen, Christopher Tan, Yan Kou, Qiaonan Duan, Zichen Wang, Gabriela Vaz Meirelles, Neil R Clark, and Avi Ma' ayan. Enrichr: interactive and collaborative html5 gene list enrichment analysis tool. BMC Bioinformatics, 14(1):128, 2013.
Yiqun Chen and James Zou. Simple and effective embedding model for single-cell biology built from chatgpt. Nature Biomedical Engineering, 9()):433449) 2025.
Abdoulatif Cissé, Xenophon Evangelopoulos, Sam Carruthers, Vladimir V Gusev, and Andrew I Cooper. Hypbo: Accelerating black-box scientific experiments using experts' hypotheses. In Pro- ceedings of the Thirty-Thing International Joint Conference on Artificial Intelligence, pp. 3881- 3889, 2024.
Tianyu Cui, Khaoula El Mekkaoui, Jaakko Reinvall, Aki S Havulinna, Pekka Marttinen, and Samuel Kaski. Gene- gene interaction detection with deep learning. Communications Biology, 5(1) 1338, 2022a.
Tianyu Cui, Aki Havulinna, Pekka Marttinen, and Samuel Kaski. Informative bayesian neural net- work priors for weak signals. Bayesian Analysis, 12141112 2022b.

---

Published as a conference paper at ICLR 2026
Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li, Tommaso Mansi, Mangal Prakash, and Rui Liao. Infosem: A deep generative model with informative priors for gene regulatory network inference. arXiv preprint arXiv:2503.04483, 2025.
Weiwei Dai, Fengting Wu, Natalie McMyn, Biena Song, Victoria E Walker-Sperling, Joseph Varri- ale, Hao Zhang. Dan H Barouch, Janer D Siliciano, Wei Li, et al. Genome-wide crispr screens identify combinations of candidate latency reversing agents for targeting the latent hiv-1 reservoir. Science translational medicine, 14(667))eabh:3351, 2022.
Jesus de la Fuente Cedeño, Robert Lehmann, Carlos Ruiz-Arenas, Jan Voges, Irene Marín-Goñi, Xabier Martinez de Morentin, David Gomez-Cabrent Idoia Ochoa, Jesper Tegnér, Vincenzo La- gani, et al. Interpretable causal representation learning for biological data in the pathway space. In The Thirteenth International Conference on Learning Representations, 2025.
James DeGregori, Gustavo Leone, Alexander Miron, Laszlo Jakoi, and Joseph R Nevins. Distinct roles for e2f proteins in cell growth control and apoptosis. Proceedings of the National Academy of Sciences, 94(14):7245-7250 1997.
Joshua Dempster, Jordan Rossen, Mariya Kazachkova, Joshua Pan, Guillaume Kugener, David Root, and Aviad T'serniak. Extracting biological insights from the project achilles genome-scale crispr screens in cancer cell lines. BioRxiv, pp. 720243, 2019.
Francesca Destefanis, Valeria Manara, and Paola Bellosta. Myc as a regulator of ribosome biogen- esis and cell competition: a link to cancer. International journal of molecular sciences, 21(11):
Jingcheng Du, Peilin Jia, Yulin Dai, Cui Tao, Zhongming Zhao, and Degui Zhi. Gene2vec: dis- tributed representation of genes based on co-expression. BMC genomics, 20(Suppl 1):82, 2019.
Louis Filstroff, liris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylmäoja, and Samuel Kaski. Targeted active learning for bayesian decision- arXiv preprint wxxv:2106.044993 2021.
Jonathan Foldager, Mikkel Jordahn, Lars K Hansen, and Michael R Andersen. On the role of model uncertainties bayesian optimisation. In Uncertainty in Artificial Intelligence, pp. 592-601. PMLR, 2023.
Peter Frazier. A tutorial bayesian optimization. arXiv preprint
2018
Rafael Bomerz-om Jennifer N Wei, David Duvenaud, José Miguel Hernández-Loben Lobato, Benjamín Sánchez-Lengelingg Dennis Sheberla, Jorge Aguilera-Iparaguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik. Automatic chemical design using data-driven contin- uous representation of molecules. ACS Central Science, 4(2):268-21) 2018.
Nate Gruver, Samuel Stanton, Nathan Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanall Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew G Wilson. Protein design with guided discrete diffusion. Advances in Neural Information Processing Systems, 36: 1249--12517 2023.
Sunil Gupta, Santu Rana, Svetha Venkatesh, et al. Bayesian optimistic optimisation with expo- nentially decaying regret. In International Conference on Machine Learning, pp. 10390-10400. PMLR, 2021.
Minsheng Hao, Yongju Lee, Hanchen Wang, Gabriele Scalia, and Aviv Regev. Perturboagent: A self-planning agent for boosting sequential perturb-seq experiments. bioRxiv, pp. 2025-05, 2025.
Winston Haynes. Bonferroni correction. In Encyclopedia systems biology, pp. 154-154. Springer, 2013.
José Miguel Hernández-Lobate Michael Gelbart. Matthew Hoffman, Ryan Adams, and Zoubin Ghahramani. Predictive entropy search for bayesian optimization with unknown constraints. In International conference on machine learning, pp. 1699-17077 PMLR, 2015.
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. Bayesian active learning for classification and preference learning. arXiv preprint arXiv: / 112.5745, 2011.
12

---

Published as a conference paper at ICLR 2026
Da Wei Huang, Brad T Sherman, and Richard A Lempicki. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research, 37(1): 1-13, 2009.
Kexin Huang. Romain Lopez, aan-Chiistian Hütter, Takamasa Kudo, Antonio Rios, and Aviv Regev. Sequential optimal experimental design of perturbation screens guided by multi-modal priors. In International Conference on Research Computational Molecular Biology, pp. 17-37. Springer, 2024.
Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, and Luigi Nardi. bo: Augmenting acquisition functions with user beliefs for bayesian optimization. arXiv preprint arXiv:2204. 10004. 2022.
Fuguo Jiang and Jennifer A Doudna. Crispr-cas9 structures and mechanisms. Annual review of biophysics, 16:505-529, 2017.
Martin Jinek, Krzysztof Chylinski, Ines Fonfara, Michael Hauer, Jennifer A Doudna, and Em- manuelle Charpentier. A programmable dual-rna-guide dna endonuclease in adaptive bacterial immunity, science, 337(6096)811-8811 2012.
Donald R Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 2144):345-383, 2001.
Donald R Jones, Matthias Schonlau, and William J Welch. Efficient global optimization of expensive black-box functions. Journal of Global optimization, 13(4):455-492, 1998.
Samuel Katz, Jian Song, Kyle P Webb, Nicolas W Lounsbury, Clare Bryant, and lain DC Fraser. Signal: A web-based iterative analysis platform integrating pathway and network approaches 2021.
optimizes hit selection from genome-scar scale assays. Cell systems, 12(4):338
Purvesh Khatri, Marina Sirota, and Atul J Butte. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Computational Biology, 8(2);e 1002375, 2012.
Yongju Lee. Dyke Ferber, Jennifer E Rood, Aviv Regev, and Jakob Nikolas Kather. How ai agents will change cancer research and oncology. Nature Cancer, 1765 2024.
Xiongquan Li, Xukang Wang, Xuhesheng Chen, Yao Lu, Hongpeng Fu, and Ying Cheng Wu. Un- labeled data selection for active learning in image classification. Scientific Reports, 14(1):424, 2024.
Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill Mesirov, and Pablo Tamayo. The molecular signatures database hallmark gene set collection. Cell systems, 1(6): 417-425, 2015.
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, and Patrick Schwab. Discobax: Discovery of optimal intervention sets in genomic experiment design. In International Conference Machine Learning, pp. 23170 23189. PMLR, 2023.
Muhammad Arslan Masood, Samuel Kaski, and Tianyu Cui. Molecular property prediction using pretrained-bert and bayesian active learning: a data-efficient approach to drug design. Journal of Cheminformatics, 17(1):58, 2025.
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, and Patrick Schwab. Genedisco: A benchmark for experimental design in drug discovery. 2021.
Svitlana Melnik, Nadine Werth, Stephane Boeuf, Eva-Maria Hahn, Tobias Gotterbarm, Martina Anton, and Wiltrud Richter. Impact of c-myc expression on proliferation, differentiation, and risk of neoplastic transformation of human mesenchymal stromal cells. Stem cell research & therapy, 10( 10(1):73,22 2019.
Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, and Samuel Kaski. Projective prefer- ential bayesian optimization. In International Conference on Machine Learning, pp. 6884-6892. PMLR, 2020.
13

---

Published as a conference paper at ICLR 2026
Jonas Mockus. The application of bayesian methods for seeking the extremum. Towards Global Optimization. 2:1 17, 1998.
Artem Moskalev, Mangal Prakash, Junjie Xu, Tianyu Cui, Rui Liao, and Tommaso Mansi. Ge- ometric hyena networks for large-scale equivariant learning. arXiv preprint 250ii:::00.2226660 2025.
Ihor Neporozhnii, Julien Roy, Emmanuel Bengio, and Jason Hartford. Efficient biological data acquisition through inference set design. 2025.
David P Nusinow, John Szpyt, Mahmoud Ghandi, Christopher M Rose, E Robert McDonald, Marian Kalocsay, Judit Jané- Valbuena, Ellen Gelfand, Devin K Schweppe, Mark Jedrychowski, et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell, 180(2):387 402, 2020.
Koji Onomoto, Kazuhide Onoguchi, and Mitsutoshi Yoneyama. Regulation of rig-i-like receptor- mediated signaling: interaction between host and viral factors. Cellular & molecular immunology, 18(3):33-5555 2021.
Adriana Ordóñez, David Ron, and Heather Harding. Protocol for iterative enrichment of integrated sgrnas via derivative crispr-cas9 libraries from genomic dna of sorted fixed cells. STAR protocols, 5(4):103493, 2024.
Aldo Pacchiano, Drausin Wulsin, Robert A Barton, and Luis Voloch. Neural design for genetic perturbation experiments. 2023.
Siddharth Ramchandran, Manuel Haussmann, and Harri Lähdesmäki. High-dimensional bayesian optimisation with gaussian process prior variational autoencoders. In International Conference on Learning Representations, 2025.
Azucena Ramos, Catherine E Koch, Yunpeng Liu-Lupo. Riley D Hellinger, Taeyoon Kyung, Keene L Abbott, Julia Fröse, Daniel Goulet, Khloe S Gordon, Keith P Eidell, et al. Leukemia- intrinsic determinants of car-t response revealed by iterative in vivo genome-wide crispr screen- ing. Nature Communications, 14(1):8048, 2023.
Jeffrey C Rathmell. T cell myc-tabolism. Immunity, 35(6):845-8466 2011.
Bing Ren, Hieu Cam, Yasuhiko Takahashi, Thomas Volkert, Jolyon Terragni, Richard A Young, and Brian David Dynlacht. E2f integrates cell cycle progression with dna repair, replication, and g2/m checkpoints. Genes & development, 16(2):245-255 256, 2002.
Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, Zachary Steinhart, Kexin Huang, Alexander Marson, Percy Liang, and Jure Leskovec. Biodiscoveryagent: An ai agent for designing genetic perturbation experiments. 2025.
Carlos G Sanchez, Christopher M Acker, Audrey Gray, Malini Varadarajan, Cheng Song, Nadire R Cochran, Steven Paula, Alicia Lindeman, Shaojian An, Gregory McAllister, et al. Genome-wide crispr screen identifies protein pathways modulating tau protein levels in neurons. Communica- tions biology, 4(1):736, 2021.
Ralf Schmidt, Zachary Steinhart, Madeline Layeghi, Jacob W Freimer, Vinh Q Nguyen, Franziska Blaeschke, and Alexander Marson. Crispr activation and interference screens in primary human cells decode cytokine regulation. bioRxiv, pp. 2021-05, 2021.
Claudia Skok Gibbs, Omar Mahmood, Richard Bonneau, and Kyunghyun Cho. Pmf-gm: vari- ational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization. Genome biology, 25(1):88, 2024.
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, and Tom Rainforth. Prediction-orrentee bayesian active learning. In International conference on artificial intelligence and statistics, pp. 7331-7348. PMLR, 2023.
Artur Souza, Luigi Nardi, Leonardo B Oliveira, Kunle Olukotun, Marius Lindauer, and Frank Hutter. Bayesian optimization with a prior for the optimum. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 265-296. Springer, 2021.
14

---

Published as a conference paper at ICLR 2026
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter. Bayesian optimization with robust bayesian neural networks. Advances in Neural Information Processing Systems, 29, 2016.
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger. Gaussian process opti- mization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning. number 1 118, pp. 1015-1022. PMLR, 2010.
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Green- side, and Andrew Gordon Wilson. Accelerating bayesian optimization for biological sequence design with denoising autoencoders. In International Conference on Machine Learning, pp- 10459-20478 PMLR, 2022.
Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, Scott L Pomeroy, Todd R Golub, Eric S Lander, et al. Gene set enrichment analysis: knowledge- approach for interpreting genome-wide expres- sion profiles. Proceedings of the National Academy of Sciences, (02(43):15545-1555)) 2005.
Damian Szklarczyk, Annika L. Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Marc Legeay, Tao Fang, Peer Bork, et al. The string database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measuremmnt sets. Nucleic acids research, 49(DI):D605-DD DD12, 2021.
William R Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3/4):255-29,, 1933.
Ruoning Wang, Christopher P Dillon, Lewis Zhichang Shi, Sandra Milasta, Robert Carter, David Finkelstein, Laura L McCormick, Patrick Fitzgerald, Hongbo Chi, Joshua Munger, et al. The transcription factor mye controls metabolic reprogramming upon lymphocyte activation. Immu- nily, (5(6):871-888)) 2011.
Christopher KI Williams and Carl Edward Rasmussen. Gaussian processes for machine learning, volume 2. MIT press Cambridge, MA, 2006.
Fan Yang, Wenchuan Wang, Fang Wang, Yuan Fang, Duyu Tang, Junzhou Huang, Hui Lu, and Jianhua Yao. scbert as a large-scale pretrained deep language model for cell type annotation of single-cell ma-seq data. Nature Machine Intelligence, 4(10):852-866, 2022.
Mehdi Yazdani-Jahromi, Mangal Prakash, Tommaso Mansi, Artem Moskalev, and Rui Liao. Helm: Hierarchical encoding for mrna language modeling. The Thirteenth International Conference Learning Representations, 2025.
Jiaqi Zhang, Louis Cammarata, Chandler Squires, Themistoklis P Sapsis, and Caroline Uhler. Active learning for optimal intervention design in causal models. Nature Machine Intelligence 5(10): 1066-1075, 2023.
Yueshan Zhao, Min Zhang, and Da Yang. Bioinformatics approaches analyzing crispr screen data: from dropout screens to single-cell crispr screens. Quantitative Biology, 10(4):307-320, 2022.
Yunkai Zhu, Fei Feng, Gaowei Hu, Yuyan Wang, Yin Yu, Yuanfei Zhu, Wei Xu, Xia Cai, Zhiping Sun, Wendong Han, et al. A gedeme-wide crispr screen identifies host factors that regulate sars- cov-2 entry. Nature communications, 12(1))961, 2021.
Xiaoxuan Zhuang, Daniel Veltri, and Eric O Long. Genome-wide crispr screen reveals cancer cell resistance nk cells induced by nk-derived ifn-y. Frontiers in immunology. 10:2879, 2019.
15

---

Published as a conference paper at ICLR 2026
A
DATA DESCRIPTION
GeneDisco contains three different embeddings: Achilles (dependency score of genetic intervention across cancer cell lines) (Dempster et al., 2019). STRING prrotein-proteio interactions) (Szklarczyk ctt al. 2021). and CCLE (quantitative proteomics information from cancer cell lines) (Nusinow et al. 2020). which are available for 17,655, 17,972, and 11,943 genes. We also consider two gene embed- dings: Gene2 Vec and GenePT, which are available for 23,940 and 61, 61,287 genes. In order to remove the effect of the different missingness levels of each gene embedding, use the 10,556 genes that have all five embeddings.
GeneDisco also contains datasets from genome-wide CRISPR assays: IFN-7, I1-2 (the log fold change of Interferon-y and Interleukin-2 production in primary human T cells (Schmidt et al.) 2021) ). Tau (Tau protein assay (Sanchez et al. 202I)), NK (Leukemia assay with NK cells (Zhuang et al. 2019)), and Sars-Covid2 (SARS-CoV-2 assay from (Zhu et al. 2021)). we consider an inter- section of genes with all modalities and each assay.
B EXPERIMENTAL DETAILS
Device details All experiments were run on Debian GNU/Linux 10 (buster) with Python 3.10.16, PyTorch 2.6.0, and CUDA 12.8. Training and inference used two NVIDIA L4 GPUs (each with 24 GB VRAM). The host machine had an AMD EPYC 7R13 processor with 192 hardware threads and 80 GB of system memory. Computations used 64-bit floating noating-point precision where required by the Bayesian layers.
Hyperparameters Unless noted, the BNN surrogate is Monte Carlo (MC) dropout neural net- work using a 2-layer MLP having a hidden width 64 and ReLU activations with dropout rate 0.5. We optimize BNNs with Adam (learning rate 77 0.001, weight decay À 0.0001) for up 200 epochs with early stopping (patience 30) on a 10% validation split; batch size was 256. The mean and variance of the posterior distribution used in acquisition functions are estimated from 100 sam- ples collected by MC dropout during testing. For modality fusion, we concatenated 1.2-normalize embeddings (Achilles, Gene2 Vec. GenePT; and where used, CCLE/STRING), Acquisition functions followed standard definitions for UCB (trade-off yn = 1), El (g 0), and TS; biology-informet variants added enrichment weights from GO or Hallmark with temperature coefficient 0.1 and B I for IFN-y and = 0.1 for IL-2.
Reproducibility and error bars For every dataset-modae independent random seeds. Plotted curves report the mean across seeds; shaded bands show + s.e.m. (standard error of the mean). Final-cycle bar plots likewise report mean s.e.m. Each BO iteration in our experiments acquires batch of 20 genes (B 20) rather than single gene, reflecting realistic experimental design.
setting we ran seven
C SENSITIVITY ANALYSIS
We analyze the sensitivity of the BO results w.r.t. , in Eq for both mean and max aggregation operation on IFN-7. We observe that both mean and max aggregation can bring the benefits of EA into BO. While performance varies across extreme 3 values, we observe that 3 in the range 1-5 generally yields the best performance across acquisition functions and datasets (Appendix C). This also expected as 3 controls the extent to which the enrichment prior influences the acquisition score. Small hence effectively removes the influence of biological structure, thus yielding poorer performance compared to using moderate 3 in the range 1-5.
16

---

Published as a conference paper at ICLR 2026
Phenotype:
IFN-7; Feature: Achilles
Acquisition
[ 0.01
3= 0.05
B =.1
B 0.5
3=1
3=5
BioEI-GO (mean)
0.0756
0.0756
0.0756
0.0756
0.0852
0.0846
BioEI-GO (max)
0.0848
0.0770
0.0770
0.0856
0.0873
0.0969
BioEI-HM (mean)
0.0756
0.0756
0.0756
0.0756
0.0763
0.0710
BioEI-HM (max)
0.0756
0,0756
0.0756
0.0760
0.0764
0.0743
BioUCB-GO (mean)
0.0850
0.0891
0.0984
0.0978
0.0975
0.0944
BioUCB-GO (max)
0.0877
0.0919
0.0956
0.0919
0,0750
0.0731
BioUCB-HM (mean)
0.0726
0,0731
0.0754
0.0816
0.0848
0.0833
BioUCB-HM (max)
0.0752
0.0747
0.0754
0.0764
0.0850
0.0836
D LLMS USAGE
Large Language Models (LLMs) were used to assist word choice and improve grammar.
E SUPPLEMENTARY EXPERIMENTAL RESULTS
E.1 CCLE AND STRING MODALITIES IN GENEDISCO
BiOUCB
B:EIHH!
BITS-00
BOTS
FNN-
STRING
IL-2 CCLE
STRING
0.04
Cumelaatow Top-k
Cumalaabie
0.22
0.23
0.02
0.22
6.1
0.31
12 Cycles
19
18
6
13 Cycles
15
38
14
Cycls
Cydees
Figure 5: CCLE and STRING modalities across datasets, Panels (left~righht; IFN-y CCLE, IFN- STRING, IL-2 CCLE, IL-2-LTRRES STRINGG Curves show base acquisitions UCB/EI/TS (solid), miology-informen variants BioUCB/BioOI//iiTTT with GO (dotted) and HM (dash-dot) in the same family color, plus Random (gray). Shaded ribbons denote mean + s.e.m.
In this section, we studied two other modalities, CCLE and STRING from GeneDisco in Figure 5) We observe that both CCLE and STRING yield substantially lower absolute recall compared to the Achilles, Gene2 Vec, and GenePT features. Moreover BO is similar to random acquisition using these two embeddings, which indicates that both CCLE and STRING are less informative predict the selected phenotype. We exclude them from the main paper and report them here for completeness. Even so, biology-informed variants provide modest, consistent gains over their bases-particullrry smaller budgets.
17

---

Published as a conference paper at ICLR 2026
E.2 BO RESULTS FOR IFN-y AND IL-2
Achilles
Gene2Ver
GenePTT
Puston
Fly
0.16
0.14
Datapen
0.30
0.0
0.00
0.00
Arquxition Cycles
Figure 6: Performance of standard BO and BioBO with three different modalities and their fusion for IFN-y (top) and IL-2 (bottom). We observe that BO with Fusion is better than BO with any single modality and BioBO that incorporates priors from enrichment analysis is better than the corresponding BO without prior.
Figure shows the complete BO results across both datasets and all four representations (Achilles, Gene2 Vec, GenePT, Fusion), where biology-informed variants (BioUCB, BioEI, BioTS) with en- richment analysis significantly exceed their base counterparts (UCB, EI, TS), and surrogate models with fused gene embeddings are better than any single modality. Improvements are most evident in early-mid cycles (better sample efficiency) and narrow later methods converge. UCB remains strong base acquisition function, and the random baseline is consistently inferior.
18

---

Published as a conference paper at ICLR 2026
E.3 CORRELATIONS BETWEEN THE PERFORMANCE OF BO AND SURROGATE MODEL
ACOM
SANICARI RAGE AELOAOOO Saumra (L4) PORVELS
SAVIONAA TApi AOC Lagapis Spaation Nacias
Covemnn No
Sonetmen CAR Tharnor
Sneman Tramore GLD
Speattirc NeoRs
+21 Cunuladee Spci
Figure 7: Relations between the performance of BO (measured by cumulative top-k recall) and surrogate model (measured by LL) on Tau, NK, and Sars-Covid2. We observed that the performance of BO more correlated with the performance of the surrogate model near optimal (LL L.L@top-1% compared with global.
Tpe Teeamore 22, Naves 0.2
Spcomee A.5 Peasern 444
Speamars 3.1 Narte dud
Speaman OL Peassrr 1552
oi
L-3: Bark facal sa APSE Gota
L-d Tape back in aMsigtep-eis
La Topic Raca pasegess
Lob Tpp facal instgupps
CO CE SO 12E
18 20
Sanimes 0.5 Porrir 427
Nearaa Norts (ill
Suranee 1.44 Porcer
Tou Topt hacelii EIIE Conal
Ta: Top face va 23-32
Tee Tp-chiaal vs 3M500 bp-5%
The Tit Racel ENCEGUO--NN
Cunuiidke Splifacal
Figure& 8: Relations between the performance of BO (measured by cumulative top-k recall) and surrogate model (measured by RMSE) on 5 datasets. We observed that the performance of BO more correlated with the performance of the surrogate model near optimal (RMSE@top-1%) compared with global.
19

---

Published as a conference paper at ICLR 2026
E.4 PURE EA RESULTS FOR TAU, NK, AND SARS-COVID2
We show the performance of experimental designs using enrichment analysis only and using Bi- oUCB for Tau, NK, and Sars-Covid2 datasets with Achilles on Figure 9 We observe that in most cases, pure EA similar to random on all three datasets, except for EA with GO on Tau where BioUCB better than UCB.
Groody Groedy
0.20
Pure Tau
Pure DA MK
Pure Sars-Covid2
Curnuattv Top Reall!
0.08
0.72
Cycles
Athilles:
Achilles:
on Achilles: Sars-Covidz
Oumuranvv Top-k Top-x Recall
0.28
0.24
0.00
12
12
Cycles
Cycles
Cycles
Figure 9: Performance of pure EA and BioUCB Achilles for Tau, NK, and Sars-Covidl
20

---

Published as a conference paper at ICLR 2026
E.5
BO RESULTS FOR TAU, NK, AND SARS-COVIDE
LCB
T5
Ranson
Bitiic- 1- .o0. Bec
960 366-HR ..... BUTS-GO --- BETSHM
Dataset: Tbu
Achilles
Gene2Vec
GenePT
Rusion
Canolaaye Tupa B1cc3
0.00
0.00
Cumulative Top Pooc
0.
0.00
12
15
16
12
15
18
18
15
18
Cydls
Cydla
Cycles
Cycles
Figure 10: Performance of BO and BioBO with three modalities and their fusion for Tau.
NK
Canuldtve Top-k Noc48
0.00
Cumuette 10p Recill
Cycles
Cycles
Cyrle6
Cycl46
Figure 11: Performance of BO and BioBO with three modalities and their fusion for NK.
F LATENT-SPACE FUSION FOR MULTIMODAL SURROGATE MODELS
To better integrate heterogeneous biological modalities in Bayesian Optimization (BO) surrogate models, implement a latent-space fusion strategy. Each modality æ 22, 23 is first projected via modality-specifff fully connected layers (with dropout for uncertainty), then fused in the latent space via either concatenation or cross-attention, followed by final Bayesian MLP predict the response:
y = ( fatf::/coos.stttttarrr/a//l/llla
or
fe3 [fc2[(onccaeenatioo([[[111 (atnr/acannnnnn((ccceeeeee22222d
This allows the surrogate to capture cross-modal interactions more effectively than simple concate- nation of raw embeddings.
21

---

Published as a conference paper at ICLR 2026
Gene2Vic
0.00
Top
OuneMrne Top Retall
0.00
Figure 12: Performance of BO and BioBO with three modalities and their fusion for Sars-Covid2.
We evaluate three surrogate variants: standard Bayesian MLP single-modall latent concatenation, and latent attention. Results for two datasets, IFN-y and IL., shown in Table 3
IFN-y
Acquisition
Bayesian MLP
Latent Concatenation
Latent Attention
Bayesian MLP
Latent Concatenation
Latent Attention
E
0.093 (0.001)
0.102 (0.001)
0.109 (0.002)
0.148 (0.002)
0.141 (0.002)
0.164 (0.002)
BieEL-GO
0.098 (0.000)
0,107 (0.000)
0,115 (0.004)
0.147 (0.003)
0.143 (0.003)
0.166 (0.003)
BioEI-HM
0.096 (0.001)
0,108 (0.001)
0.116 (0.003)
0.153 (0.002)
0.154 (0.002)
0.174 (0.002)
TS
0.083 (0.001)
0.099 (0.003)
0.107 (0.001)
0.142 (0.001)
0.142 (0.002)
0.166 (0.004)
BioTS-GO
0.095 (0.001)
0.105 (0.001)
0.109 (0.002)
0.147 (0.003)
0.139 (0.001)
0.160 (0.002)
BioTS-HI HM
0.097 (0.001)
0,1 (0.003)
0,124 (0.002)
0,153 (0.002)
0.154 (0.002)
0.175 (0.003)
UCB
0.100 (0.001)
0.102 (0.003)
0.113 (0.000)
0.174 (0.001)
0.155 (0.001)
0.169 (0.003)
BioUCB-GO
0.102 (0.001)
0,101 (0.004)
0.116 (0.003)
0,169 (0.001)
0.173 (0.003)
0.173 (0.002)
BioUCB-HM
0.109 (0.001)
0.112 (0.002)
0.127 (0.003)
0,178 (0.001)
0.168 (0.002)
0.176 (0.005)
Table Comparison of BO performance using different latent-space fusion strategies on IFN-y and IL2 datasets (mean + std over 5 seeds).
Latent attention consistently outperforms latent concatenation and the Bayesian MLP across acqui- sition functions for both datasets, particularly for BioUCB-HM, where cross cross-modal interactions are critical. Latent concatenation also improves over the single-modal MLP, confirming the benefit of integrating multiple modalities. These results support the claim in the main text that multimodality fusion enhances BO efficiency.
G RUNTIME COMPARISON
We report average runtime per iteration (evaluating 20 genes per eycle) for BioBO and baseline BO methods over all datasets. All experiments were run standard GPU (NVIDIA A10). The table includes variants with and without multimodal fusion and enrichment analysis (EA) to show the computational overhead introduced by these components. While multimodal fusion and EA slightly increase runtime compared to and practical for typical high-through throughut CRISPR experiments.
models, the additional cost remains modest
H SENSITIVITY TO THE TOP-K % THRESHOLD ENRICHMENT ANALYSIS
We evaluate the effect of different top-k% thresholds used for enrichment analysis. varying from 5% to 50% on the IFN-y dataset (Achilles features). As shown in Table BioBO remains robust for k between 5-20%, exhibiting only minor performance variation. Larger thresholds (30-50%)
22

---

Published as a conference paper at ICLR 2026
Method
Avg Runtime per BO Cycle (8)
UCB (Achilles)
8,55
UCB (Fusion)
10.50
BioUCB-HM (Fusion)
12.45
EI (Achilles)
7.64
EI (Fusion)
12.57
BioEI-HN (Fusion)
13.05
TS (Achilles)
6,95
TS (Fusion)
12.18
BioTS-HM (Fusion)
12.87
Table 4: Runtime per iteration for BioBO and baseline BO methods, averaged over datasets. Variants with multimodal fusion and/or enrichment analysis (EA) are included to show the overhead of these components.
dilute the enrichment signal by including broader, noisier set of genes, leading to slightly reduced BO performance. We use k = 10% as a practical default.
Top-k%
5%
15%
20%
50%
BioEI-GO 0.090 + 0.001 0.085 0.006 0.084 + 0.009
0.085 + 0.010
0.074 + 0.002 0.071 +: 0.001
Table 5: Sensitivity of BioBO to top-k% used for enrichment analysis. Performance shown as cumulative top-k recall.
I INTERPRETABILITY CASE STUDY ON IL-2 DATASET
To assess whether the interpretability benefits of BioBO generalize beyond the IFN-y setting, we analyze the IL-2 immune-cell CRISPR perturbation dataset. The results mirror the IFN-y findings: baseline UCB recovers several relevant pathways but with modest enrichment strength, whereas BioUCB-HM identifies the same pathways with higher overlap and stronger statistical significance.
Table 6: Enrichment analysis on IL-2 dataset comparing UCB and BioUCB-HM.
Baseline UCB
Pathway
Overlap
Adjusted p-value
Odds Ratio
Combined Score
49/200
6.16
10-27
11.512
738.585
E2F TARGETS
36/200
8.76 x 10
7.004
248.528
26/200
2.01 x 10
4.436
80,423
22/150
2.41
10-7
5.028
88.784
BioUCR-HM (Fusion + EA)
MYC.TARGETS.WW
179/200
2.39 x 10 231
487.252
260596
E2F TARGETS 41/200
2.45 10 10-12
4.962
148.099
MYC.TARGETS.VV 18/58
1.79 x 10
8.081
166.006
32/200
5.50 x 10-7
3.518
59.221
These results show that BioUCB-HM not only recovers all pathways identified by UCB but also enhances their enrichment signal by several orders of magnitude. Mechanistically, these path- ways-MYC, E2F, G2M checkpoint, and DNA repair-govern central processes lymphocyte metabolism and proliferation (Ren et al.) 2002 DeGregori et al. 1997 Wang et al. 2011 Rath- mell 201T). The stronger enrichment observed under BioUCB HM reflects its ability to prioritize perturbations that align with the regulatory circuitry of smmune-cell activation. providing deeper mechanistic insight into pathway-leve drivers. This analysis was also validated by two independent immunology domain experts.
23

---

Published as a conference paper at ICLR 2026
J FAILURE CASES: MISMATCHED ENRICHMENT PATHWAYS
Although enrichment analysis substantially strengthens acquisition when the pathway database is relevant to the biological context, we also evaluate failure cases where the enrichment prior is mismatched. Specifically, we apply oncology-focused pathways ("ONC") to guide the design of immune-cell perturbations. Because these pathways are not related to immune signaling, the enrich- ment prior becomes non-informative and may slightly bias the acquisition towards irrelevant genes. In such settings, BioBO effectively falls back to the multimodal surrogate model, resulting in little or improvement over the baseline BO acquisition.
Table 7: Failure-case comparison on IFN-y: correct enrichment prior (GO) vs. mismatched oncol- ogy prior (ONC).
Method
Fusion
Achilles
GPT
Gene2vec
EI
0.093 (0.001)
0.072 (0.001)
0.077 (0.004)
0.071 (0.006)
BioEI-GO (correct)
0.098 (0.000)
0.085 (0.000)
0.095 (0.005)
0.079 (0.004)
BioEI-ONC (mismatched)
0.091 (0.001)
0.074 (0.002)
0.077 (0.008)
0.073 (0.006)
These results reinforce the practical takeaway: BioBO provides strong gains when pathway knowl- edge is biologically aligned with the experimental setting, while remaining robust when the prior noisy cettaatchhdcconssssteee with our theoretical no-harm guarantee.
K ROBUSTNESS TO MISSING MODALITIES
To assess BioBO's robustness in practical scenarios where some embedding modalities are unavail- able, we simulate missing data on the IFN-y dataset by dropping selected modalities and imputing missing embeddings with KNN. Table g summarizes the performance (mean + std over seeds) across three acquisition functions. Fusion remains consistently superior to single-modality surro- gates even under LNN-imputation, indicating that heterogeneous embeddings provide complemen- tary biological signal and that BioBO remains usable when embeddings are partially missing common situation large-scale perturbation screens.
Table 8: Performance when some modalities are missing on IFN-y dataset. KNN-imputation is used for missing embeddings.
Modality
EI
UCB
TS
Fusion (all modalities; 18,344 genes)
0.046 = 0.003
0.060 = 0.001
0.048 = 0.002
Achilles only
0.040 = 0.001
0.042 0.002
0.041 = 0.001
GPT only
0.034 + 0.002
0.050 + 0.008
0.041 = 0.008
Gene2Vec only
0.028 + 0.003
0.035 0.003
0.033 = 0.003
These results show that multimodal fusion remains advantageous even under partially missing data, reflecting the complementary structure of gene-level biological embeddings and supporting the prac- tical deployability of BioBO.
L INTERPRETATION OF ENRICHMENT PARAMETERS AND B
The temperature parameter and the prior strength 3 control the contribution of enrichment analysis (EA) to the acquisition function relative to the uncertainty of the surrogate model. Conceptually, t determines how concentrated the enrichment-derived prior is across candidate genes: t - oo, the prior becomes uniform, and EA is ignored (pure exploration). whereas as + 0, the prior becomes sharply peaked, emphasizing top-ranked genes (heavy exploitation). The parameter 3 modulates the weight of this prior within the acquisition: very small 3 effectively removes the influence of EA, while extremely large 3 over-amplifies the enrichment signals. Empirically, moderate values of
24

---

Published as a conference paper at ICLR 2026
t and | provide stable performance, balancing exploitation of enriched pathways with exploration guided by the surrogate model. This discussion complements the main text and provides practical guidance for setting these hyperparameters.
M ENSEMBLING MULTIPLE ENRICHMENT SOURCES
BioBO is fully compatible with ensembling multiple enrichment sources because the x-BO prior formulation allows additive or multiplicative aggregation of priors. While the main text reports GO and Hallmark (HM) separately for clarity, conducted experiments using ensemble prior that averages enrichment-derivee scores from both databases ("BioEI-GOHM"), Table 9 summarizes the performance across modalities on the IFN-y dataset.
Table 9: Performance of BioBO with multiple enrichment sources. BioEI-GOHM averages GO and Hallmark priors, showing consistent improvement over individual priors.
Method
Fusion
Achilles
GPT
Gene2
EI
0.093 (0.001)
0.072 (0.001)
0.077 (0.004)
0.071 (0.006)
BioEI-GO
0.098 (0,000)
0.085 (0.000)
0.095 (0.005)
0.079 (0.004)
BioEI-HM
0.096 (0.001)
0.076 (0.001)
0.096 (0.007)
0.079 (0.002)
BioEI-GOHM
0.101 (0.002) 0.092 (0.004)
0.093 (0.008)
0.084 (0.004)
These results demonstrate that BioBO naturally leverage complementary strengths from multiple enrichment sources, and ensemble priors consistently match or outperform individual priors. More dynamic weighting strategies for combining enrichment sources are a promising direction for future work.
N FURTHER INTERPRETABILITY ANALYSIS OF UNDEREXPLORED BIOLOGICALLY NOVEL GENES PRIORITIZED BY BIOBO
Beyond the well-known MYC/E2F modules reported in the main text, BioBO prioritized set of underexplored genes whose knockouts produced top 0. % IFN-7 increases. These include FAU, MAK16, PCBP2, and multiple ribosomal proteins (e.g.. RPL,19, RPL.27, RPI337, RPS11, RPS13, RPS17, RPS20). These genes are not typically highlighted by baseline BO, yet they form coher- module downstream of MYC-driven ribosome biogenesis, key regulator of T-cell growth and effector differentiation (Destefanis et al., 2020). Perturbation of ribosomal components induces nu- cleolar stress and NF-xBBp53 activation (Akef et al. 2020), shifting cells from proliferation toward higher cytokine output. PCBP2 further modulates MAVS/RIG-I signaling Onomoto et al. (2021) linking directly to interferon pathways.
Two independent domain experts reviewed and validated this mechanistic interpretation. This anal- ysis provides concrete examples of how BioBO's enrichment-infomen acquisition can reveal bio- logically meaningful, underexplored targets, complementing standard BO approaches.
O ADDITIONAL RELATIONAL ANALYSIS BETWEEN BO PERFORMANCE AND SURROGATE MODELING
We measure global LL across all genes and observe weak or even negative correlation with BO performance. This arises because global LL is dominated by the dense region of low-response genes, whereas BO only depends on the surrogate a small neighborhood the maximizer. In our CRISPR datasets, only small fraction of genes have high response to IFN-y IL-2. A surrogate that fits the bulk region extremely well (high global LL) but underestimates the tails can perform worse in BO than one that slightly sacrifices global LL but better resolves the local geometry near the optimum. When we restrict LL to the top-k genes (in terms of ground truth response), the correlation with BO performance becomes positive and substantially stronger (see Appendix X), confirming that local surrogate quality near the optimum, rather than global goodness-of fit, is what drives BO.
25

---

Published as a conference paper at ICLR 2026
Table 10: Conditional correlation analysis for IFN-y across fusion strategies and acquisition func- tions.
Fusion
Method
Global
LL@10%
LL
LL@15
None
EI
0.211
0.202
0.212
TS
0.038
0.038
0.059
0.153
UCB
-0.065
0.285
0.315
0.356
Random
0.008
+0.009
-0.035
Input concar.
EI
-0.018
0.313
TS
0.096
0.163
0.169
0.198
LCB
-0.054
0,362
0.357
0.355
Random
0.209
0.037
0.003
-0.037
HI 0.078
0.246
TS
0.140
0,145
0.205
UCB
-0.067
0.273
0.299
0.364
Random
0.181
0.008
0.009
-0035
Latent attention
EI
-0.021
0.216
TS
0.070
0.151
0.178
0.270
Random
0.181
0.008
-0,009
-0.035
Table 11: Conditional correlation analysis for IL-2 across fusion strategies and acquisition functions.
Fusion
Method
LL Global
LL
LL@5%
LL@P19 1%%
None
EI
-0.152
0.455
0.471
0.479
TS
0.007
0.325
0.362
0.386
UCB
-0.139
0.493
0.515
0.515
Random
0.083
0.224
0,233
0.208
Input concat.
EI
-0.173
0.572
0.581
0.568
-0.023
0.439
0.457
0.441
UCB
-0.217
0.540
0.559
0.546
Random
0.088
0.274
0.278
0.248
Latent concat.
EI -0.197
0.524
0,542
0.526
0.375
UCB
-0.192
0.479
0,506
0.500
Random
0.088
0.224
0.233
0.208
Latent attention
EI
-0.153
0.552
0.568
0.559
-0.058
LCB
-0.171
0.514
0.532
0.530
Random
0.088
0.224
0.233
0.208
P ADJUSTED CUMULATIVE TOP-K RECALL WITH + 1.96 S.E.M.
26

---

Published as a conference paper at ICLR 2026
Table 12: Cumulative top-k recall with 1.96 s.e.m. of each acquisition function on different datasets. We observe that BioBO achieves the best performance on 23/24 different settings, and B-MUCB-HM with surrogate function using fused features achieves the best performance for both IyN-y and IL-2. The best performance (with the smallest standard error) is bold.
Phenotype: IFN-y
Fusion
Achilles
GenePT
Gene2
0.093 (0.002)
0.072 (0.002)
0.077 (0.008)
0.071 (0.011)
BioEI-GO (ours)
0.098 (0.001)
0.085 (0.001)
0.095 (0.010)
0.079 (0.008)
BioEI-HM (ours)
0.096 (0.002)
0.076 (0.002)
0.096 (0.014)
0.079 (0.004)
TS
0.083 (0.002)
0.068 (0.002)
0.088 (0.004)
0.073 (0.004)
BioTS-GO (ours)
0.095 (0.002)
0.073 (0,001)
0.097 (0.008)
0.095 (0.009)
BioTS-HM (ours)
0.097 (0.002)
0.097 (0.009)
0.093 (0.010)
0.081 (0.008)
UCB
0.100 (0.002)
0.077 (0.002)
0.086 (0.008)
0.093 (0.010)
BioUCB- (ours)
0.102 (0.002)
0.098 (0.004)
0.092 (0.010)
0.098 (0.004)
BioUCB- HMM (ours)
0.109 (0.002)
0.085 (0.006)
0.101 (0.002)
0.103 (0.008)
Random
0.050 (0.002)
0.050 (0.002)
0.050 (0.002)
0.050 (0.002)
Phenotype: IL-2
Fusion
Achilles
GenePT
Gene2
0,148 (0.004)
0.130 (0.006)
0.107 (0.010)
0.109 (0.004)
BioEI-GO (ours)
0.147 (0.006)
0.138 (0.006)
0.107 (0.010)
0.115 (0.004)
BioEI-HM (ours)
0.153 (0.003)
0.130 (0.005)
0.107 (0.009)
0.109 (0.004)
TS
0.142 (0.002)
0.119 (0.002)
0.113 (0.027)
0.113 (0.004)
BioTS-GO (ours)
0.147 (0.005)
0.142 (0.004)
0.119 (0.021)
0.119 (0.002)
BioTS-HM (ours)
0.153 (0.004)
0.123 (0.007)
0.139 (0.025)
0.124 (0.004)
UCB
0,174 (0.002)
0.143 (0.006)
0.118 (0.022)
0.123 (0.001)
BioUCB-Ge (ours)
0.169 (0.002)
0.158 (0.002)
0.131 (0.015)
0.133 (0.004)
BioUCB-HM (ours)
0.178 (0.002)
0.163 (0.002)
0.138 (0.023)
0.127 (0.001)
Random
0.049 (0.002)
0.048 (0.002)
0.049 (0.002)
0.046 (0.003)
27


Reviews:
[{"review_id": "4FyoF1KplO", "reviewer": "Reviewer_FsM3", "summary": "This paper proposes to enhance Bayesian optimization for genomic perturbation experiment design with multi-modal gene embeddings and enrichment analysis, essentially incorporating biological knowledge as prior to Bayesian optimization.", "questions": "Please see above", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The methodology component of the paper is generally easy to follow and clearly presented. Figure 1 is particularly helpful for a high level perception of the proposed framework.\n\n- Ablation studies are presented in the experiment section, which helps the understanding of each component's contribution.", "weaknesses": "- Novelty: I think novelty of the proposed methodology is lacking. This work is more about how to incorporate biological knowledge and analysis into existing methods such as $\\pi$BO. I think sometimes combining existing methods could be interesting, but if it's more of a realization of an approach in a specific domain, I think it's less interesting. To be honest I'm a little surprised that this work was submitted to this venue instead of some computational biology journal, since it has a quite heavy dose of biological content and analysis.\n\n- Clarity & practicality: Although the methodology component of the paper is generally easy to follow and clearly presented, I find the biological experiment setup not so easy to follow (as a person who does not have abundant biology knowledge). From Figure 1, it seems that there is an online component of the UCB update (wet lab perturbations) being conducted, but I assume this is not actually carried out in the experiment section? How are the unlabeled perturbations evaluated online given a dataset in the experiment? And is the proposed method with wet lab perturbation actually practical given the significant efficiency concern with 20 cycles?", "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": "wLGRxb2PED", "reviewer": "Reviewer_tp66", "summary": "This paper proposes Biology-Informed Bayesian Optimization (BioBO) for designing genomic perturbation experiments. The method makes two main contributions: (1) integrating multimodal gene embeddings (Achilles, Gene2Vec, GenePT) to improve surrogate modeling, and (2) augmenting acquisition functions with biological priors from enrichment analysis using the πBO framework. Experiments on GeneDisco benchmarks show 25-40% improvement in labeling efficiency compared to standard BO, with the method identifying biologically coherent pathways. The authors demonstrate that multimodal embeddings improve surrogate model performance particularly near the optimum, and provide theoretical no-harm guarantees for their enrichment-augmented acquisition strategy.", "questions": "1.\tCan you provide more detailed analysis or ablation studies on why multimodal fusion specifically helps near the optimum but not globally? What properties of the fused representation drive this?\n2.\tThe DiscoBAX performance is substantially worse than reported in Lyle et al. 2023. Can you investigate and explain this discrepancy? Is it due to implementation differences, hyperparameters, or dataset characteristics?\n3.\tHow sensitive are the results to the choice of top-k% for enrichment analysis? You use 10% but provide no justification or sensitivity analysis for this critical hyperparameter.\n4.\tCan you provide computational cost comparisons between BioBO and baseline methods? How does the enrichment analysis overhead scale?\n5.\tFor the biological interpretation (Table 2), can you provide additional case studies beyond IFN-γ? Are there cases where the method fails to identify biologically meaningful pathways?\n6.\tHow does performance change when some modalities are missing? This is a practical scenario but not addressed.\n7.\tHave you validated any of the predicted gene perturbations experimentally or with domain experts? The biological interpretations would be more convincing with external validation.\n8.\tWhy use simple concatenation for fusion rather than more sophisticated approaches like attention-based fusion or learned gating mechanisms? Have you experimented with alternatives?\n9.\tThe beta parameter shows significant impact on performance (Appendix C). Can you provide principled guidelines for setting beta, or ideally, an adaptive approach?\n10.\tHow does the method perform with batch acquisition (B > 1), which is more common in practical CRISPR screens?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe combination of multimodal gene embeddings with enrichment-based priors in BO is novel for this application domain. The analysis showing that surrogate model performance near the optimum (rather than globally) correlates with BO performance provides useful insights.\n2.\tThe experimental evaluation is thorough, covering five datasets across multiple modalities and acquisition functions with proper error estimation. The theoretical grounding via πBO provides principled guarantees. The correlation analysis between surrogate quality and BO performance is well-executed.\n3.\tThe paper clearly motivates the problem and explains why existing BO methods for gene perturbation are limited. The two-pronged approach (better embeddings + biological priors) is logical and well-presented.\n4.\tThe problem of efficient gene perturbation design is important for drug discovery. The 25-40% improvement in labeling efficiency could translate to meaningful cost savings. The interpretability aspect through enrichment analysis is valuable for practitioners.", "weaknesses": "1.\tLimited Technical Novelty: The core techniques are straightforward applications of existing methods. Simple concatenation for multimodal fusion is the most basic approach; more sophisticated fusion architectures (attention mechanisms, learned fusion) are not explored. The enrichment prior construction (Eq. 6) is relatively simple. The paper primarily validates that combining these existing techniques works for this domain rather than introducing new methodology.\n2.\tInsufficient Biological Validation: Only one detailed biological case study (IFN-γ, Table 2) is provided. The biological interpretations feel somewhat post-hoc rather than predictive. The paper would benefit from: (1) validation with domain experts, (2) wet-lab validation of predictions, (3) deeper mechanistic explanations beyond pathway enrichment, and (4) analysis of failure cases where the method selects biologically implausible genes.\n3.\tExperimental Limitations: The experiments are restricted to GeneDisco datasets which represent a limited evaluation scope. Key concerns include: (1) DiscoBAX performing poorly contradicts Lyle et al. 2023 without adequate explanation, (2) high variance in some results suggests instability, (3) no comparison with other multimodal fusion approaches or alternative biology-informed methods beyond pure EA, (4) computational costs are not reported, (5) the choice of hyperparameters (beta, temperature, top-10%) lacks principled justification.\n4.\tAnalysis Depth: The correlation analysis reveals interesting patterns but lacks deeper explanation. Why does global LL negatively correlate with BO performance? Why does fusion specifically help near the optimum? The paper states these observations but does not provide mechanistic understanding. The no-harm guarantee is asymptotic, but finite-sample behavior is not analyzed. The sensitivity to beta (Appendix C) shows significant variation that undermines the robustness claims.\n5.\tScalability and Generalization: All experiments use the same 10,556 genes common across all embeddings, which is a restricted setting. How does the method perform with missing modalities? What about other organisms beyond human? The batch size is always 1, but practical screens often label batches; how does this affect the approach? The paper does not address these practical considerations.\n6.\tPresentation Issues: While generally clear, some aspects need improvement: Figure 3 and similar correlation plots are central to the argument but the negative global correlation is counterintuitive and poorly explained. Some claims are overstated (e.g., \"dramatically stronger enrichment signals\" in Section 4.5 when comparing different experimental settings). The main paper could better synthesize the extensive appendix results.", "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": "7ZtdW2IeLi", "reviewer": "Reviewer_cXha", "summary": "The paper proposes BioBO* a biology-informed Bayesian optimization framework for efficient design of genomic perturbation experiments. The goal is to prioritize which gene knockouts to test in costly CRISPR assays. BioBO integrates two main ideas:\n1. **Multimodal gene embeddings (Fusion)** – combining Achilles (functional dependency), Gene2Vec (co-expression), and GenePT (text-derived) representations to improve surrogate modeling.\n2. **Enrichment analysis (EA)** – incorporating biologically meaningful pathway priors into acquisition functions via the π-BO framework (Hvarfner et al., 2022), guiding the search toward biologically coherent gene clusters.\n\nThe method is evaluated on five CRISPR datasets from GeneDisco, showing 25–40% labeling efficiency improvement over standard BO, and stronger enrichment signals in discovered pathways. The authors also provide theoretical guarantees that integrating EA priors does not worsen regret asymptotically.", "questions": "1. **Prior calibration**: How sensitive are the results to the choice of temperature parameter *t* and β in Eq. 4 and Eq. 6? The appendix shows some analysis, but can the authors comment on interpretability of these hyperparameters across datasets?\n2. **Fusion mechanism**: Have the authors considered learning the fusion weights (e.g., via gating or attention) instead of static concatenation? Would that risk overfitting given small training sets per iteration?\n3. **Pathway dependence**: Since GO and Hallmark differ in coverage and granularity, could BioBO dynamically switch or ensemble multiple enrichment sources?\n4. **Scalability**: How would BioBO scale to genome-scale design (>20k genes) when multiple priors and embeddings are used? Are there computational bottlenecks?\n5. **Biological interpretability**: Can the authors provide further examples where enrichment priors led to selection of underexplored but biologically novel genes, beyond MYC/E2F modules?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Strong biological motivation**: Addresses a key challenge in experimental design for genomics, where exhaustive perturbation is infeasible.\n- **Principled integration of biology and machine learning**: The use of the π-BO formalism to incorporate enrichment priors is theoretically grounded and non-trivial.\n- **Novel multimodal surrogate modeling**: Demonstrates empirically that fusing heterogeneous biological embeddings (Achilles + Gene2Vec + GenePT) yields better performance near optima.\n- **Comprehensive experiments**: The evaluation on multiple datasets (IFN-γ, IL-2, Tau, NK, SARS-CoV-2) with ablations and sensitivity analyses is thorough and carefully executed.\n- **Interpretability and biological validation**: The enrichment results (e.g., MYC and E2F target pathways) provide mechanistic insight into immune regulation and show that the discovered perturbations are biologically meaningful.\n- **No-harm theoretical guarantee**: Retains the asymptotic regret bounds of standard BO, ensuring robustness to potentially noisy enrichment priors.", "weaknesses": "- **Limited novelty in BO theory**: The core algorithmic novelty is the integration of known biological priors within an existing π-BO framework; the BO component itself remains myopic and standard.\n- **Empirical improvements modest in some datasets**: While fusion provides consistent gains, improvements are smaller or marginal in certain conditions (e.g., BioEI on IL-2), suggesting dataset-dependent benefits.\n- **Dependence on predefined pathway databases**: The approach relies on GO and Hallmark annotations; biases or incompleteness in these resources may limit generalization, especially in less-studied cell types.\n- **Simple fusion strategy**: Concatenation is effective but basic. More principled fusion (e.g., learned or attention-based integration) could enhance representation quality.\n- **No analysis of computational cost**: Training BNN surrogates with multimodal embeddings and enrichment updates may be computationally heavier than standard BO, but runtime comparisons are not reported.", "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": "OIDLwLbLRN", "reviewer": "Reviewer_ohwq", "summary": "This paper proposes _Biology-Informed Bayesian Optimization (BioBO)_, which addresses challenges in drug discovery that require genomic intervention experiments. The authors introduce two key innovations: (1) a multimodal gene representation to improve the surrogate model in Bayesian Optimization (BO), and (2) the integration of enrichment analysis (EA) into a biology-prior–augmented acquisition function through a principled πBO framework. This integration provides a theoretical “no-worse” guarantee and establishes a foundation for incorporating broader sources of biological knowledge in similar ways.\n\nExperiments on real-world problems demonstrate the effectiveness of both proposed components — the multimodal embeddings and the biological priors. The results produced by BioBO are also biologically interpretable.", "questions": "1. The multimodal design appears to involve simple concatenation of multiple gene embeddings. Is there a specific reason for choosing this approach to combine different embeddings?\n    \n2. There is little discussion of the individual contributions of each gene embedding or the rationale for selecting each. Could the authors provide more insight into how one should choose among available gene embeddings?\n    \n3. If I understand correctly, the so-called “biological prior” is derived from statistical patterns in the training data, rather than from external biological knowledge. Could the authors clarify how EA can still enhance BO performance under this setup, even without access to external priors?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- The paper addresses a well-motivated and highly important problem.\n    \n- It integrates EA-based experimental design common in biology into the πBO framework,  bridging knowledge from two domains, and offers a principled non-worse theoretical guarantee.\n    \n- The ablation studies sufficiently demonstrate the contributions of the multimodal representations and the EA integration. **Furthermore, the genes selected by BioBO align with known biological interpretations of central regulatory genes, indicating strong real-world potential.**\n    \n- The multimodal surrogate model is modular, allowing a wide range of biological and machine learning encoders, which enhances its practicality for real-world applications.", "weaknesses": "- According to the appendix, the width of the error bars is ±1 s.e.m., which is less convincing compared to the standard ±1.96 s.e.m. used for 5% significance. Could the authors justify this choice?\n\n- _(Minor)_ The generality of the method is limited. Although the proposed approach is termed “Biology-Informed BO,” it applies primarily to biological problems where EA is feasible. The authors might consider adjusting the algorithm’s name to better reflect its domain specificity.", "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)