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.


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!

Speakers & Panelists

Silvia Chiappa

Silvia Chiappa

Google DeepMind
James Zou

James Zou

Stanford University
Jason Hartford

Jason Hartford

Lindsay Edwards

Lindsay Edwards

CTO, Relation Therapeutics
Nicola Richmond

Nicola Richmond

VP, BenevolentAI
Kyunghyun Cho

Kyunghyun Cho

NYU, Genentech
Michael Bronstein

Michael Bronstein

University of Oxford
Brian Hie

Brian Hie

Stanford University
Bianca Dumitrascu

Bianca Dumitrascu

Columbia University

Schedule (CET)

Time Title Presenter
09:00 - 09:15 Opening Remarks
09:15 - 09:50 (Invited Speaker) Functional Causal Bayesian Optimization and DiscoGen for Learning Optimal Interventions and Inferring Gene Regulatory Networks
Silvia Chiappa
09:50 - 10:00 Coffee Break
10:00 - 10:35 (Invited Speaker) Leveraging (natural) language models for biology
James Y Zou
10:40 - 11:00 (Oral Paper) DNA-DIFFUSION: Leveraging generative models for controlling chromatin accessibility and gene expression via synthetic regulatory elements
Luca Pinello
11:05 - 11:25 (Oral Paper) Dirichlet flow matching with applications to DNA sequence design
Gabriele Corso
11:25 - 11:40 Break and Poster Setup
11:40 - 13:40 Poster Session and Lunch (provided)
13:40 - 14:40 Panel Discussion
Kyunghyun Cho, Lindsay Edwards, Nicola Richmond, Bianca Dumitrascu, Michael Bronstein, Aïcha Bentaieb
14:45 - 15:20 (Invited Speaker) Efficiently detecting interactions from high dimensional observations of pairwise perturbations
Jason Hartford
15:20 - 15:35 Coffee Break
15:35 - 15:55 (Oral Paper) Season combinatorial intervention predictions with Salt & Peper
Thomas Gaudelet
15:55 - 16:15 (Oral Paper) A mechanistically interpretable neural-network architecture for discovery of regulatory genomics
Alex M Tseng
16:20 - 16:55 (Invited Speaker) Evo: Long-context modeling from molecular to genome scale
Brian Hie
16:55 - 17:00 Closing Remarks


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

Technical Committee

  • Aaron Lee Feller
  • Aashish Jain
  • Abhimanyu Banerjee
  • Aicha BenTaieb
  • Alex M Tseng
  • Alexander Lin
  • Alexander P Wu
  • Ali Saadat
  • Alistair Turcan
  • Alma Andersson
  • Ami Hashemi
  • Amy X. Xie
  • Anna Lobley
  • Ayush Agrawal
  • Baoyi Zhang
  • Chang Hu
  • Changxin Wan
  • Chengzhong Ye
  • Chenyu Wang
  • Chunyu Yuan
  • Doruk Cakmakci
  • Eric Nguyen
  • Gaurav Mishra
  • Ghulam Murtaza
  • Hejin Huang
  • Huangqingbo Sun
  • Hyewon Jeong
  • Jay Shah
  • Ke Ni
  • Lama Moukheiber
  • Lars Lorch
  • Lechuan Li
  • Liangze Jiang
  • Lingchao Mao
  • Liu Cao
  • Martin Rohbeck
  • Masaru Koido
  • Mayank Jindal
  • Mengbo Wang
  • Minxue Jia
  • Moksh Jain
  • Muyu Yang
  • Nagaraj Thenkarai Janakiraman
  • Nathan Wan
  • Nathaniel Lee Diamant
  • Neel Jaydip Gandhi
  • Olivia Mendivil Ramos
  • Oznur Tastan
  • Pavel Avdeyev
  • Peiman Mohseni
  • Pouya M. Ghari
  • Renyu Zhang
  • Rizwan Qureshi
  • Romain Lopez
  • Ruchir Rastogi
  • Ruiqi Lyu
  • Russell Littman
  • Sandra Batista
  • Sayan Ghosal
  • Sepideh Maleki
  • Seyednami Niyakan
  • Sharmi Banerjee
  • Shreyas Joshi
  • Shuvom Sadhuka
  • Shyaman Jayasundara
  • Sivaramakrishnan Sankarapandian
  • Soufiane Mourragui
  • Srinivasan Sivanandan
  • Stephanie Muller
  • Surag Nair
  • Sushil Thapa
  • Talip Ucar
  • Tingyang Yu
  • Ujani Hazra
  • Wei Qiu
  • Wenbin Guo
  • Wentao Guo
  • William Connell
  • Xiang Zhang
  • Xingfan Huang
  • Xinhao Liu
  • Xinming Tu
  • Yanay Rosen
  • Yanshuo Chen
  • Yashas Annadani
  • Yijia Xiao
  • Yimin Liu
  • Ying Jin
  • Ying Yang
  • Yiqun Wang
  • Yongju Lee
  • You Wu
  • YU Yingying
  • Yuan Wang
  • Yuheng Fu
  • Yunzhe Jiang
  • Zhaoying Pan
  • Ziqing Lu
  • Ziteng Liu