- Decision is out✨
- Camera-ready deadline: July 21th, 2023
- Workshop time: Saturday, July 29th, 2023
- Location: Hawaii Convention Center (Room 314), Honolulu, Hawai'i, USA
Each year, machine learning (ML) advances are successfully translated to develop systems we now use regularly, such as speech recognition platforms or translation software. The COVID-19 pandemic has highlighted the urgency for translating these advances to the domain of biomedicine. Biological data has unique properties (high dimensionality, degree of noise and variability), and therefore poses new challenges and opportunities for methods development. To facilitate progress toward long-term therapeutic strategies or basic biological discovery, it is critical to bring together practitioners at the intersection of computation, ML, and biology. The ICML Workshop on Computational Biology (WCB) will highlight how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. This workshop thus aims to bring together interdisciplinary ML researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community. We especially encourage interdisciplinary submissions that might not neatly fit into one of these categories.
Deadline for paper submissions: May 17th 19th 2023 (11:59 PM Pacific Time)
Reviewer deadline: June 9th 2023
Notification of acceptance: June 19th, 2023
Camera-ready deadline: July 21th, 2023
Workshop date: July 29th, 2023
We invite extended abstracts dealing with novel algorithms and computational approaches that are robust,
scalable to high-dimensional data, and provide interpretable models of biological systems. These can be applications of ML methods or
bioinformatics approaches to biological and biomedical data or novel approaches that enable new analyses. Papers will be presented in poster
format and some will be selected for oral presentation (contributed and spotlight talks).
Through invited talks and presentations by the participants, this workshop will bring together current advances in Computational Biology and set
the stage for continuing interdisciplinary research discussions.
Submission
All novel Computational Biology approaches are of interest to the workshop.
In addition to the main track, we will hold a special track on “Explainability in Biological data” which will feature submissions focused on interpretable ML approaches and their applications for biological data. This will include inherently explainable and interpretable ML approaches, post-hoc interpretations of existing models, ways to evaluate the quality of explanations, limitations and failure modes of existing methods, and visualization strategies for analyzing models.
We welcome original abstracts on preliminary ideas and findings in the following format:
* Times below are in HST (GMT-10)
9:00 - 9:05AM | Opening Remarks | ||
Session 1 (Chair: Cameron Park) | |||
9:05 - 9:40AM | Michael Bronstein | ||
Geometric ML for designing new molecules | |||
9:40 - 10:00AM | William Beardall | ||
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer | [Paper] | ||
10:00 - 10:15AM | Spotlight talks | ||
Claudia Skok Gibbs - A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix Factorization | [Paper] | ||
Eric D Sun - Graph reinforcement and smoothing for improved spatial gene expression prediction | [Paper] | ||
Jos Torge - DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping | [Paper] | ||
10:15 - 10:45AM | Coffee break | ||
Session 2 (Chair: Lingting Shi) | |||
10:45 - 11:05AM | Rui Yang | ||
HiC2Self: Self-supervised Hi-C contact map denoising | [Paper] | ||
11:05 - 11:25AM | Sukwon Yun | ||
Single-cell RNA-seq data imputation using Feature Propagation | [Paper] | ||
11:25 - 11:50AM | Spotlight talks | ||
Kristina Ulicna - Learning dynamic image representations for self-supervised cell cycle annotation | [Paper] | ||
Nhat Khang Ngo - Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures | [Paper] | ||
Cuong Q Nguyen - Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation | [Paper] | ||
Kavi Gupta - Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing | |||
Simon V Mathis - Normal Mode Diffusion: Towards Dynamics-informed Protein Design | [Paper] | ||
11:50 - 12:50PM | Poster session 1 | ||
12:50 - 1:30PM | Lunch break | ||
Session 3 (Chair: Bianca M. Dumitrascu) | |||
1:30 - 2:05PM | Bin Yu | ||
Veridical data science for reliable, reproducible, and transparent data analysis and decision-making | |||
2:05 - 2:25PM | Spotlight talks | ||
Zhongliang Zhou - Explaining the blackbox: Unraveling Protein Language Model's Learning Mechanisms for Kinase-Specific Phosphorylation Prediction | |||
Jun Xia - Why Deep Models Often Cannot Beat Non-deep Counterparts on Molecular Property Prediction? | |||
Alex M Tseng - Hierarchically branched diffusion models for scientific discovery | [Paper] | ||
Kemal Inecik - scARE: Attribution Regularization for Single Cell Representation Learning | [Paper] | ||
2:25 - 3:00PM | Invited talk: Su-In Lee | ||
3:00 - 4:00PM | Poster session 2 | ||
4:05 - 4:15PM | Award ceremony and concluding remarks |
*Listed alphabetically
DeepMind Professor of Artificial Intelligence
Department of Computer Science
University of Oxford
Professor
Paul G. Allen School of Computer Science
University of Washington, Seattle
Chancellor's Distinguished Professor
Department of Statistics & EECS
UC Berkeley
ICML Workshop on Computational Biology aims to foster an inclusive and welcoming community.
If you have any questions, comments, or concerns, please reach out to workshopcompbio@gmail.com.
We are also featuring other workshops that you might find helpful for diversity and inclusion.
Queer in AI
Dana Pe’er (mskcc.org/research/ski/labs/dana-pe-er)
Debora Marks (https://www.deboramarkslab.com/)
Alexander Anderson (labpages.moffitt.org/andersona/)
Elham Azizi (https://www.azizilab.com/ )
Sandhya Prabhakaran (sandhyaprabhakaran.com)
Abdoulaye Baniré Diallo (labo.bioinfo.uqam.ca )
Wesley Tansey (http://wesleytansey.com)
Bianca Dumitrascu (https://computational-morphogenomics-group.github.io)
Maria Brbic (https://brbiclab.epfl.ch/)
Yubin Xie: yux2009@med.cornell.edu
Cassandra Burdziak: burdziac@mskcc.org
Mafalda Dias: mafalda.dias@crg.eu
Cameron Park: cyp2111@columbia.edu
Pascal Notin: pascal.notin@cs.ox.ac.uk
Joy Fan: lf2684@columbia.edu
Ruben Weizman: rubenweitzman@gmail.com
Lingting Shi: ls3456@columbia.edu
Siyu He: sh3846@columbia.edu
Yinuo Jin: yj2589@columbia.edu
Achille Nazaret, Columbia University
Adam Gayoso, UC Berkeley
Adrian Heilbut, logphase research
Ahmed Halioui, Mt Intelligent Machines
Ahmet Coskun, Georgia Tech
Alice Bizeul, ETHZ
Alyssa Morrow, UC Berkeley
Amine Remita, Université du Québec à Montréal
Amy Xie, Memorial Sloan Kettering Cancer Center
Anastasiya Belyaeva, Google Research
Arnav Das, University of Washington
Ayshwarya Subramanian, Broad Institute
Behrooz Tahmasebi, MIT
Benjamin Gallusser, EPFL
Benjamin Wesley, Columbia University
Bianca Dumitrascu, Columbia University
Bishnu Sarker, Inria
Bo Yuan, Harvard University
Brian Trippe, Columbia University
Cameron Park, Columbia University
Cassandra Burdziak, Memorial Sloan Kettering Cancer Center
Chandana Rajesh, Cold Spring Harbor Laboratory
Charles Harris, University of Cambridge
Chenlian Fu, Memorial Sloan Kettering Cancer Center
Chetanya Pandya, 2seventy bio
Christopher Garay, Paradigm4
Dana Pe'er, Memorial Sloan Kettering Cancer Center
Debora Marks, Harvard University
Delasa Aghamirzaie, Natera
Ece Ozkan, MIT
Ehsan Hajiramezanali, Genentech
Elham Azizi, Columbia University
Elior Rahmani, UCLA
Engelbert Mephu Nguifo, Université Clermont Auvergne, CNRS, LIMOS
Evan Seitz, Cold Spring Harbor Laboratory
Eyal Itskovits, GSK.ai
Fabian Theis, Helmholtz Zentrum München
Filippo Utro, IBM THOMAS J. WATSON RESEARCH CENTER
Freddie Bickford Smith, University of Oxford
Gabriele Scalia, Genentech
Han Spinner, Harvard
Hayda Almeida, UQAM
Ignacio Vazquez-Garcia, Memorial Sloan Kettering Cancer Center / Columbia University
Ilyes Baali, Memorial Sloan Kettering Cancer Center
Jan Brauner, University of Oxford
Jason Dou, University of Pittsburgh
Jessica White, Memorial Sloan Kettering Cancer Center
Jill Gallaher, Moffitt Cancer Center
Joy Fan, Columbia University
Justin Hong, Columbia University
Kangway Chuang, Genentech Research and Early Development
Kevin Hoffer-Hawlik, Columbia University
Keyur Shah, Massachusetts General Hospital
Khanh Dinh, Columbia University
Kyle Swanson, Stanford University
Lea Goetz, GSK.ai
Lei Xiong, MIT
Lingting Shi, Columbia University
Lingyi Cai, Columbia University
Lood van Niekerk, Harvard Medical School
Luis Rueda, University of Windsor
Manu Setty, SKI
Maria Brbic, Stanford University
Mario Wieser, Genedata AG
Matthew Peterson, Paradigm4
Mervin Fansler, Memorial Sloan Kettering Cancer Center
Mingxuan Zhang, Columbia University
Mohammad Lotfollahi, Helmholtz Zentrum München
Mostafa Karimi, Amazon
Namkyeong Lee, Korea Advanced Institute of Science and Technology
Nathan Rollins, Seismic Therapeutic
Nazim Bouatta, Harvard University
Nicolas Beltran, Columbia University
Petko Fiziev, Illumina, Inc
Philip Fradkin, Vector Institute
Pierre Boyeau, UC Berkeley
Pooja Kathail, UC Berkeley
Ragothaman Yennamalli, SASTRA Deemed University
Ričards Marcinkevičs, ETH Zurich
Rishabh Anand National, University of Singapore
Romain Lopez, Genentech and Stanford
Rui Yang, Memorial Sloan Kettering Cancer Center
Russell Kunes, Columbia University
Sabeur Aridhi, LORIA
Sairam Behera, Baylor College of Medicine
Sandhya Prabhakaran, Moffitt Cancer Center
Sepideh Maleki, Genentech
Sheng Liu, Indiana University School of Medicine
Shiyi Yang, UC Berkeley
Shouvik Mani, Columbia University
Shreshth Malik, University of Oxford
Shuhao Zhang, EPFL
Simon Mathis, University of Cambridge
Siyu He, Columbia University
Smita Krishnaswamy, Yale University
Somesh Mohapatra, Caterpillar Inc
Soufiane Mourragui, Hubrecht Institute
Soumya Kundu, Stanford University
Srivamshi Pittala, Katana Graph Inc
Stephen Zhang, University of Melbourne
Surag Nair, Stanford University
Talip Ucar, AstraZeneca
Tianming Zhou, Carnegie Mellon University
Umberto Lupo, EPFL
Victor Greiff, University of Oslo
Warith Eddine, DJEDDI FST Manar
Xiang Niu, Memorial Sloan Kettering Cancer Center
Xue Long Zhao, University of Pennsylvania
Xuecong Fu, Carnegie Mellon University
Yanay Rosen, Stanford University
Yinuo Jin, Columbia University
Yongju Lee, Genentech
Yubin Xie, Memorial Sloan Kettering Cancer Center
Yue Wu, Stanford University
Ziqi Tang, Cold Spring Harbor Laboratory