Main Track
Submissions spanning machine learning methodology and biologically grounded applications at the intersection of ML, biology, and genomics; no strict page limit for main text.
Machine Learning/CORE Unranked
MLGenX 2026
ICLR 2026 Workshop on Machine Learning for Genomics Explorations
Feb 26 2026Riocentro Convention and Event Center, Rio de Janeiro, BrazilOfficial workshop site Site reachable
MLGenX 2026 is an ICLR workshop focused on bridging machine learning and functional genomics to advance target identification in biology and drug discovery. The 2026 theme, 'From Reasoning to Experimentation: Closing the Loop Between AI Agents and the Biological Lab,' emphasizes adaptive, interpretable AI systems that integrate with experimental feedback to drive biological insight.
Key deadlines
Verified September 9, 2026Full paper
February 9, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
February 9, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Special Track on Lab-in-the-Loop and Self-Evolving Systems
Submissions focused on systems that learn through interaction with experimental feedback; no strict page limit for main text.
Tiny Papers Track
Short (up to 4-page) submissions presenting early-stage ideas, negative results, position papers, or preliminary findings; AI-generated papers not permitted.
AI-Generated Track
Submissions where AI systems serve as a substantial contributor or primary author under human oversight; must align with Main or Special Track themes, max 8 pages for main text, and must disclose AI role.
Research areas in scope
01
Main Track
Foundation models and agentic AI: scalable training and fine-tuning, in-context learning for biological and multi-omics data, and agentic systems for reasoning and tool useCausality and mechanistic interpretability: causal discovery, counterfactual modeling, and biologically grounded representations to reveal mechanism rather than correlationGeneralizability and uncertainty quantification: robust transfer across biological domains, modalities, and experimental settingsDesign of regulatory sequence elements: ML for DNA, RNA, and gene or cell therapeutics, including sequence-to-function modeling and AI-assisted CRISPR and RNA designPerturbative biology and cellular organization: modeling cellular responses to perturbations and inferring cell states, cell–cell interactions, and tissue organization from multi-omics data
02
Special Track on Lab-in-the-Loop and Self-Evolving Systems
Active learning with experimental feedback: models that adapt based on wet-lab or simulation outcomes, optimizing experimental design and discovery cyclesLab-in-the-loop architectures: integration of AI reasoning systems with robotic labs, automated pipelines, and real-time data streamsSelf-evolving agentic frameworks: autonomous agents capable of iterative hypothesis generation, testing, and refinement using foundation models and multi-agent coordination
Policies worth checking twice
- Submissions are double-blind and hosted on OpenReview.
- Submissions identical to previously published or accepted main ICLR conference papers are not allowed.
- Papers previously posted on non-peer-reviewed websites (e.g., arXiv) are permitted.
- Submission to archival repositories like arXiv is allowed during the review period.
- Authors may submit works currently under review by other venues.
- Accepted workshop papers are non-archival and may be subsequently published in conferences or journals.
- AI-generated papers are not permitted in the Tiny Papers Track.
- Submissions to the AI-Generated Track must clearly disclose the role of AI systems and human contributors and comply with ICLR policies on large language model usage and research ethics.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026