mlsanityData to 7 Oct 2026

DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design

arXiv 2312.04064 ↗First posted 7 Dec 2023q-bio.QM cs.LG stat.MEcited by 12

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Abstract (arXiv)

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the billions of potential interventions to maximize the expected influence on the target phenotype. However, to reduce the risk of failure in future stages of trials, practical experiment design aims to find a set of interventions that maximally change a target phenotype via diverse mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the rate of significant discoveries per experiment while simultaneously probing for a wide range of diverse mechanisms during a genomic experiment campaign. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation covering both synthetic as well as real-world experimental design tasks. DiscoBAX outperforms existing state-of-the-art methods for experimental design, selecting effective and diverse perturbations in biological systems.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 17 of its 54 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

17 of 54 references.

References · 17

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5 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 2.

12 papers.

  1. 2024BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experimentscited by 49
  2. 2025LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?cited by 10
  3. 2024Automated Discovery of Pairwise Interactions from Unstructured Datacited by 8
  4. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  5. 2025In-silico biological discovery with large perturbation modelscited by 4
  6. 2024Practical Bayesian Algorithm Execution via Posterior Samplingcited by 2
  7. 2025BioBO: Biology-informed Bayesian Optimization for Perturbation Designcited by 2
  8. 2026Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphscited by 1
  9. 2025Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeuticscited by 0
  10. 2025Efficient Data Selection for Training Genomic Perturbation Modelscited by 0
  11. 2025g-DPO: Scalable Preference Optimization for Protein Language Modelscited by 0
  12. 2026Many Needles in a Haystack: Active Hit Discovery for Perturbation Experimentscited by 0