Generative AI in Genomics (Gen^2): Barriers and Frontiers

Pinar Demetci, Maria Skoularidou, Dongshunyi Li, Valentin De Bortoli, Tamara Broderick, Max Welling, Arnaud Doucet, Renzo Soatto

Keywords: generative AI, genomics, AI for science, computational biology

TL;DR: Advancing generative AI in genomics for tangible biological impact by identifying domain-specific challenges and promising directions.

Abstract: Generative AI (GenAI) is transforming biology, with breakthrough applications like directed evolution in protein science. The parallel ambition to engineer cellular and tissue states in genomics is now a major frontier, yet progress is hampered by domain-specific roadblocks. Our workshop is designed to bridge this gap between GenAI's promise and its practical applications towards this goal. With recent large-scale data initiatives launched to support GenAI models creating an inflection point for the field, timing is ideal. Through a field-grounding keynote by a genomics expert, invited talks by GenAI practitioners, contributed presentations, and a moderated debate, we will bring together experts and early-career scientists from machine learning and experimental genomics to collaboratively define a roadmap for progress. Our program will target core, interconnected challenges across the development pipeline: from data generation priorities and model design for genomic hierarchies to biologically-grounded evaluation frameworks and interpretability. By defining promising research directions and critical evaluations, our ultimate goal is to catalyze a new generation of models for tangible biological impact.

Submission Number: 145