Machine Learning for Genomics Explorations (MLGenX)

Despite rapid advances in data-driven biology, our limited understanding of the biological mechanisms underlying diseases continues to hinder therapeutic innovation. While genomics and multi-omics platforms have generated vast datasets, translating these into actionable biological insights remains an open challenge. At the same time, the emergence of foundation models and AI agents capable of reasoning, planning, and hypothesis generation offers a unique opportunity to reimagine how we approach discovery in biology. The 3rd MLGenX workshop aims to bring together the machine learning, genomics, and biology communities to explore this new frontier. This year’s theme, From Reasoning to Experimentation: Closing the Loop Between AI Agents and the Biological Lab, focuses on adaptive, interpretable, and experiment-aware AI systems that learn from feedback and drive biological insight. By fostering interdisciplinary collaboration, benchmark sharing, and open discussion, MLGenX 2026 aims to chart the path toward lab-in-the-loop science and accelerate innovation in biology and drug discovery.

Theme (2026): From Reasoning to Experimentation: Closing the Loop Between AI Agents and the Biological Lab