Machine learning advances are used in self-driving cars, speech recognition systems, and translation software. However, the COVID-19 pandemic has highlighted the urgency of translating such advances to the domain of biomedicine. Such a pivot requires new machine learning methods to build long-term vaccines and therapeutic strategies, predict immune avoidance, and better repurpose small molecules as drugs.The ICML Workshop on Computational Biology (WCB) will highlight how machine learning approaches can be tailored to making both translational and basic scientific discoveries with biological data. Practitioners at the intersection of computation, machine learning, and biology are in a unique position to frame problems in biomedicine, from drug discovery to vaccination risk scores, and WCB will showcase such recent research. Commodity lab techniques lead to the proliferation of large complex datasets and require new methods to interpret these collections of high-dimensional biological data, such as genetic sequences, cellular features or protein structures and imaging datasets. These data can be used to make new predictions towards clinical response, uncover new biology, or aid in drug discovery.This workshop aims to bring together interdisciplinary machine learning 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.
| Opening remarks (Talk) | |
| Predicting and maximizing genomic variant discovery via Bayesian nonparametrics (Invited Talk) | |
| Predicting and maximizing genomic variant discovery via Bayesian nonparametrics (Q&A) | |
| Generative power of a protein language model trained on multiple sequence alignments (Contributed Talk) | |
| Generative power of a protein language model trained on multiple sequence alignments (Q&A) | |
| RITA: a Study on Scaling Up Generative Protein Sequence Models (Spotlight) | |
| Learning Batch-Invariant Representations with Domain Adaptation in Large Scale Proteomics Data (Spotlight) | |
| COEM: Cross-Modal Embedding for MetaCell Identification (Spotlight) | |
| Break | |
| Towards a Common Coordinate Framework: Alignment of Spatially Resolved Omics Data (Invited Talk) | |
| Towards a Common Coordinate Framework: Alignment of Spatially Resolved Omics Data (Q&A) | |
| BayesTME: A reference-free Bayesian method for end-to-end analysis of spatial transcriptomic data (Contributed Talk) | |
| BayesTME: A reference-free Bayesian method for end-to-end analysis of spatial transcriptomic data (Q&A) | |
| EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (Spotlight) | |
| Learning to rank metabolites across datasets (Spotlight) | |
| 7-UP: generating in silico CODEX from a small set of immunofluorescence markers (Spotlight) | |
| RTfold: RNA secondary structure prediction using deep learning with domain inductive bias (Spotlight) | |
| A Deep Learning Framework for Estimating Cell-specific Kinetic Rates of RNA Velocity (Spotlight) | |
| Poster session #1 / Lunch break (Poster Session) | |
| Panel: ML for drug discovery (Panel Discussion) | |
| Probabilistic basis decomposition for characterizing temporal dynamics of gene expression (Spotlight) | |
| SNVformer: An Attention-based Deep Neural Network for GWAS Data (Spotlight) | |
| Extracting Part of Signal Representation from Direct RNA Squiggle for Modification Detection (Spotlight) | |
| A mechanistic probabilistic model of genomic compartments (Spotlight) | |
| TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-binding analyses (Spotlight) | |
| Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (Spotlight) | |
| Assessing the utility of genomic deep learning models for disease-relevant variant effect prediction (Spotlight) | |
| Molecular Fingerprints Are a Simple Yet Effective Solution to the Drug–Drug Interaction Problem (Spotlight) | |
| Poster session #2 / Break (Poster Session) | |
| DIISCO: Dynamic Intercellular Interactions in Single Cell transcriptOmics (Talk) | |
| DIISCO: Dynamic Intercellular Interactions in Single Cell transcriptOmics (Q&A) | |
| SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles. (Contributed Talk) | |
| SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles (Q&A) | |
| Concluding remarks (Talk) | |