The 2021 ICML Workshop on Computational Biology

Accepted Submissions

Contributed Talks

Paper 3: Hannes Stärk, Christian Dallago, Michael Heinzinger and Burkhard Rost. Light Attention Predicts Protein Location from the Language of Life. [paper]
Paper 44: Mohammad Lotfollahi, Anastasia Litinetskaya and Fabian Theis. Multigrate: single-cell multi-omic data integration [paper]
Paper 57: Khalil Ouardini, Romain Lopez, Matthew G Jones, Sebastian Prillo, Richard Zhang, Michael I. Jordan and Nir Yosef. Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data [paper]
Paper 67: Berk Alpay, Mafalda Dias, Jonathan Frazer, and Debora Marks. A Bayesian Mutation-Selection Model of Evolutionary Constraints on Coding Sequences

Spotlights & Posters

Paper 8 : Mengyan Zhang and Cheng Soon Ong. Opportunities and Challenges in Designing Genomic Sequences [paper]
Paper 15: Bowen Jing, Stephan Eismann, Pratham Soni and Ron Dror. Equivariant Graph Neural Networks for 3D Macromolecular Structure [paper]
Paper 21: Charlotte Bunne, Vignesh Ram Somnath and Andreas Krause. Multi-Scale Representation Learning on Proteins [paper]
Paper 24: Gilles Gut, Stefan Stark, Gunnar Ratsch and Natalie Davidson. pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules [paper]
Paper 34: Ren Wang, Tianqi Chen, Stephen Lindsly, Cooper Stansbury, Indika Rajapakse and Alfred Hero. Immuno-mimetic Deep Neural Networks (Immuno-Net) [paper]
Paper 35: Manik Kuchroo, Abhinav Godavarthi, Guy Wolf and Smita Krishnaswamy. Multimodal data visualization, denoising and clustering with integrated diffusion [paper]
Paper 46 Michelle Li and Marinka Zitnik. Deep Contextual Learners for Protein Networks [paper 46]
Paper 60: Soumitra Pal, Brian Oliver and Teresa Przytycka. Gene expression evolution across species, organs and sexes in Drosophila
Paper 53: Nicole Thadani, Nathan Rollins, Sarah Gurev, Pascal Notin and Debora Marks. Viral Evolution and Antibody Escape Mutations using Deep Generative Models [paper]
Paper 56: Antonio Majdandzic and Peter Koo. Statistical correction of input gradients for black box models trained with categorical input features [paper]

Posters

Paper 10: Jun Cheng, Carolin Lawrence and Mathias Niepert VEGN: variant effect prediction with graph neural network [paper]
Paper 11: Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay, Zoe Jaeckel, Florian Steenbergen, Stefanie Hardung, Ilka Diester and Joschka Boedecker. NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding [paper]
Paper 12: Keisuke Yamada and Michiaki Hamada. Prediction of RNA-protein Interactions Using a Nucleotide Language Model
Paper 13: Hasib Zunair and Abdessamad Hamza. Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis [paper]
Paper 20: Soroor Hediyeh-Zadeh, Yi Xie, Holly Whitfield and Melissa Davis Reference-free cell type annotation and phenotype characterisation in single cell RNA sequencing by learning geneset representations
Paper 22: Amine Amor, Pietro Liò, Ramon Viñas, Helena Andres Terre and Vikash Ranjan Singh Graph Representation Learning on Tissue-Specific Multi-Omics [paper]
Paper 25: Subhabrata Majumdar, Saonli Basu, Matt McGue and Snigdhansu Chatterjee Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data [paper]
Paper 27: Alice Del Vecchio, Andreea Deac, Pietro Liò and Petar Veličković Neural message passing for joint paratope-epitope prediction
Paper 28: Rohan Ghotra, Nicholas Lee, Rohit Tripathy and Peter Koo Designing Interpretable Convolution-Based Hybrid Networks for Genomics [paper]
Paper 29: Zhenqin Wu, Bryant Chhun, Galina Schmunk, Syuan-Ming Guo, Chang Kim, Li-Hao Yeh, Tomasz Nowakowski, James Zou and Shalin Mehta DynaMorph: self-supervised learning of morphodynamic states of live cells [paper]
Paper 30: Rishal Aggarwal, Akash Gupta and U. Deva Priyakumar APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design [paper]
Paper 38: Soroor Hediyeh-Zadeh, Daryl Wilding-McBride, Ahmed Mohamed, Rune Larsen, Melissa Davis and Andrew Webb. Improving confident peptide identifications across mass spectrometry runs by learning deep representations of TIMS-MS1 features
Paper 39: Xueer Chen, Linyue Fan, Cameron Y. Park, Lauren Friend, Sham Rampersaud, George Plitas, Alexander Y. Rudensky and Elham Azizi. Semi-supervised Deconvolution of Spatial Transcriptomics in Breast Tumors
Paper 41: Arnav Das*, Rui Yang*, Vianne Gao, Alireza Karbalaghareh, William Noble, Jeff Bilmes and Christina Leslie Epiphany: Predicting the Hi-C Contact Map from 1D Epigenomic Data [paper]
Paper 42: Ji-Eun Park, Wancen Mu, Yining Jiao, Michael Love, Marc Niethammer and Natalie Stanley. MultImp: Multiomics Generative Models for Data Imputation [paper]
Paper 45: Yang An, Felix Drost, Fabian Theis, Benjamin Schubert and Mohammad Lotfollahi. Deconvolution of the T cell immune response using multi-modal learning [paper]
Paper 47: Guojie Zhong, Jiayao Wang, Siyu He and Xi Fu. Towards better understanding of developmental disorders from integration of spatial single-cell transcriptomics and epigenomics [paper]
Paper 48: Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Mika Jain, Mirjana Efremova, Sarah Teichmann, Vaibhav Rajan and Xiuwei Zhang. Integrating unpaired scRNA-seq and scATAC-seq with unequal cell type compositions [paper]
Paper 49: Andrea Karlova, Wim Dehaen and Daniel Svozil. Fingerprint VAE [paper]
Paper 50: Elior Rahmani, Eran Halperin, Michael Jordan and Nir Yosef. Identifying systematic variation in gene-gene interactions at the single-cell level by leveraging low-resolution population-level data
Paper 52: Boning Li, Yingce Xia, Shufang Xie, Lijun Wu and Tao Qin. Distance-Enhanced Graph Neural Network for Link Prediction [paper]
Paper 54: Somesh Mohapatra, Joyce An and Rafael Gómez-Bombarelli. Graph attribution methods applied to understanding immunogenicity in glycans [paper]
Paper 55: Bishnu Sarker, Marie-Dominique Devignes, Sabeur Aridhi and Guy Wolf. Prot-A-GAN: Automatic Protein Function Annotation using GAN-inspired Knowledge Graph Embedding [paper]
Paper 58: Nicholas Lee and Peter Koo. Representation learning of genomic sequence motifs via information maximization [paper]
Paper 59: Michael Cai, Seojin Bang and Heewook Lee. TCR-epitope binding affinity prediction using multi-head self attention model [paper]
Paper 61: Nate Gruver, Samuel Stanton, Polina Kirichenko, Marc Finzi, Phillip Maffettone, Vivek Myers, Emily Delaney, Peyton Greenside and Andrew Wilson. Effective Surrogate Models for Protein Design with Bayesian Optimization [paper]
Paper 62: Anirudh Jain, Markus Heinonen and Samuel Kaski. Multi-target optimization for drug discovery using generative models [paper]
Paper 63: Otto Kißig, Martin Taraz, Sarel Cohen and Tobias Friedrich. Drug Repurposing using Link Prediction on Knowledge Graphs [paper]
Paper 64: Mafalda Dias, Pascal Notin, Jonathan Frazer, Sam Berry, Nikki Thadani, Lood van Niekerk, Debora Marks and Yarin Gal. Exploring the latent space of deep generative models: Applications to G-protein coupled receptors
Paper 66: Mika Sarkin Jain, Krzysztof Polanski, Mirjana Efremova and Sarah Teichmann. MultiMAP: Dimensionality Reduction and Integration of Multimodal Data [paper]

Highlights of Published Work

Paper 2: Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh and Ranadip Pal. Representation of Features as Images with Neighborhood Dependencies forCompatibility with Convolutional Neural Networks [paper]
Paper 4: Betul Guvenc Paltun, Samuel Kaski and Hiroshi Mamitsuka. DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction [paper]
Paper 7: Jacob Schreiber and William Noble. Data-driven Experimental Prioritization via Imputation and Submodular Optimization [paper]
Paper 9: Yan Gao and Yan Cui. Data Inequality, Machine Learning and Health Disparity [paper]
Paper 26: An Zheng, Michael Lamkin, Hanqing Zhao, Cynthia Wu, Hao Su and Melissa Gymrek. Deep neural networks identify sequence context features predictive of transcription factor binding
Paper 40: Ilia Igashov, Kliment Olechnovič, Maria Kadukova, Česlovas Venclovas and Sergei Grudinin. VoroCNN: Deep Convolutional Neural Network Built on 3D Voronoi Tessellation of Protein Structures [paper]
Paper 43: Ilia Igashov, Nikita Pavlichenko and Sergei Grudinin. Spherical Convolutions on Molecular Graphs for Protein Model Quality Assessment [paper]