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.

Submission Instructions

Similar to the main ICLR conference, submissions will be double blind. We use OpenReview to host papers. There will be a strict upper limit of 6 pages for the main text of the submission, with unlimited additional pages for citations and appendices. To prepare your submission, please use the ICLR template style.

Submissions that are identical to versions that have been previously published, or accepted to the main ICLR conference are not allowed. However, papers that cite previous related work by the authors and papers that have appeared on non-peer reviewed websites (like arXiv) do not violate the policy. Submission of the paper to archival repositories such as arXiv is allowed during the review period.

Note: Authors are permitted to submit works that are currently under review by other venues. Additionally, accepted papers are not considered archival and can be subsequently published in other conferences or journals.

We plan to offer Best Paper Award(s), and exceptional submissions will be chosen for oral presentations. Please note that while our workshop is not archival, accepted papers will be featured on the workshop website.

Note: Official reviews are anonymous, and unlike the main ICLR conference track, the papers and reviews are not made public until acceptance!

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 4 February 8, 2024
  • Acceptance Notification: March 3, 2024
  • Workshop Date: Saturday, May 11, 2024 (in-person)


Speakers & Panelists

Silvia Chiappa

Silvia Chiappa

Google DeepMind
Bo Wang

Bo Wang

U of Toronto
Su-In Lee

Su-In Lee

U of Washington
Juan Caicedo

Juan Caicedo

U of Wisconsin-Madison, Broad
Max Jaderberg

Max Jaderberg

CAIO, Isomorphic Labs
Nicola Richmond

Nicola Richmond

VP, BenevolentAI
Kyunghyun Cho

Kyunghyun Cho

NYU, Genentech
Jason Hartford

Jason Hartford

Recursion
Jure Leskovec

Jure Leskovec

Stanford
Marinka Zitnik

Marinka Zitnik

Harvard University
Lindsay Edwards

Lindsay Edwards

CTO, Relation Therapeutics
Bianca Dumitrascu

Bianca Dumitrascu

U of Cambridge

Organizers

Ehsan Hajiramezanali
Ehsan Hajiramezanali
Genentech
Aviv Regev
Aviv Regev
EVP, Genentech
Fabian Theis
Fabian Theis
Helmholtz Munich
Maria Brbic
Maria Brbic
EPFL
Charlotte Bunne
Charlotte Bunne
ETH Zurich
Arman Hasanzadeh
Arman Hasanzadeh
Google
Tommaso Biancalani
Tommaso Biancalani
Genentech
Eric Nguyen
Eric Nguyen
Stanford
Aïcha Bentaieb
Aïcha Bentaieb
Genentech
Chandler Squires
Chandler Squires
MIT
Sepideh Maleki
Sepideh Maleki
Genentech
Alex Tseng
Alex Tseng
Genentech
Ying Jin
Ying Jin
Stanford
Gabriele Scalia
Gabriele Scalia
Genentech
Moksh Jain
Moksh Jain
Mila
Nathaniel Diamant
Nathaniel Diamant
Genentech
Yashas Annadani
Yashas Annadani
TUM


Technical Committee

TBD