Machine Learning for Genomics Explorations (MLGenX)

The main objective of this workshop is to bridge the gap between machine learning (ML) and functional genomics (Gen), focusing on target identification---a pivotal aspect of drug discovery. Our goal is to explore this challenging aspect of modern drug development, where we aim to identify biological targets that play a critical role in modulating diseases. We will delve into the intersection of ML and genomics-related topics, with a specific focus on areas where the availability of data has expanded due to emerging technologies (e.g., large-scale genomic screens, single cell, and spatial omics platforms). From a biological perspective, our discussions will encompass sequence design, molecular perturbations, single cell, and spatial omics, shedding light on key biological questions in target identification. On the ML front, we aim to address topics such as interpretability, foundation models for genomics/biology, generalizability, and causal discovery, emphasizing the significance of ML in advancing target identification.

Overview

The critical bottleneck in drug discovery is still our limited understanding of the biological mechanisms underlying diseases. Consequently, often we do not know why patients develop specific diseases, and many drug candidates fail in clinical trials. Recent advancements in new genomics platforms and the development of diverse omics datasets have ignited a growing interest in the study of this field. In addition, machine learning plays a pivotal role in improving success rates in language processing, image analysis, and molecular design. The boundaries between these two domains are becoming increasingly blurred, particularly with the emergence of modern foundation models that stand at the intersection of data-driven approaches, self-supervised techniques, and genomic explorations. This workshop aims to elucidate the intricate relationship between genomics, target identification, and fundamental machine learning methods. By strengthening the connection between machine learning and target identification via genomics, new possibilities for interdisciplinary research in these areas will emerge.

The goal of this workshop is to bring together communities at the intersection of machine learning and genomics to discuss areas of interaction and explore possibilities for future areas of research. During this workshop, participants will gain valuable insights into the synergies between ML and genomics-related research, and help refine the next generation of applied and theoretical ML methods for target identification. We look forward to your participation in this exciting discourse on the future of (foundational) genomics and AI.

Call for Papers

We consider a broad range of subject areas including but not limited to the following topics:

Foundation models for genomics
Biological sequence design
Interpretability and Generalizability in genomics
Causal representation learning
Perturbation biology
Modeling long-range dependencies in sequences, single-cell and spatial omics
Integrating multimodal perturbation readouts
Active learning in genomics
Generative models in Biology
Multimodal representation learning
Uncertainty quantification
Optimal transport
Experimental design for Biology
Graph neural network and knowledge graph
New datasets and benchmarks for genomics explorations

Both contributions introducing new ML methods to existing problems and those that highlighting and explaining open problems are welcome. We also encourage submissions related to application of molecular biology, including but not limited to, single-cell RNA analysis, bulk RNA studies, proteomics, and microscopy imaging of cells and/or tissues.

Important Dates

All deadlines are 11:59 pm UTC -12h ("Anywhere on Earth"). All authors must have an OpenReview profile when submitting.

Submission Deadline: ~~February 8, 2024~~
Acceptance Notification: ~~March 3, 2024~~
Camera-Ready Deadline: April 26, 2024
Workshop Date: Saturday, May 11, 2024 (in-person)

Workshop Registration

Whether you're a seasoned professional or a curious enthusiast, all are welcome to attend! Don't worry if you don't have an accepted paper – participation is open to everyone.

If you have already registered for ICLR, you can join us at the MLGenX workshop. However, if you're solely interested in the workshop, you can still participate in the MLGenX workshop by registering for the "Saturday Workshop 1 Day Pass". Please visit this link to secure your spot.

We look forward to meeting you in Vienna!