Machine Learning for Genomics Explorations (MLGenX)

Fabian J Theis, Aviv Regev, Arman Hasanzadeh, Mengdi Wang, Ehsan Hajiramezanali, Sara Mostafavi, Tommaso Biancalani

Keywords: Machine Learning, Genomics, Target Discovery, Pharma, Foundation Models, AI Agents

TL;DR: Bringing together communities at the intersection of machine learning and genomics to discuss areas of interaction and explore future research possibilities.

Abstract: Our limited understanding of the biological mechanisms underlying diseases remains a critical bottleneck in drug discovery. As a result, we often lack insights into why patients develop specific conditions, leading to the failure of many drug candidates in clinical trials. Recent advancements in genomics platforms and the emergence of diverse omics datasets have sparked increasing interest in this field. The primary objective of this workshop is to bridge the gap between machine learning and genomics, emphasizing target identification and emerging drug modalities such as gene and cell therapies and RNA-based drugs. By fostering interdisciplinary collaboration, we aim to advance the integration of these disciplines and accelerate innovation in drug discovery.

Submission Number: 101