The 2020 ICML Workshop on Computational Biology

Invited speakers

*Listed alphabetically

Accepted Submissions

Contributed Talks

Paper 20: Geoff Fudenberg, David R Kelley and Katherine S Pollard.  Predicting 3D genome folding from DNA sequence [paper] [slideslive]
Paper 11: Guillaume Jaume, Pushpak Pati, Antonio Foncubierta Rodriguez, Jean-Philippe Thiran, Orcun Goksel and Maria Gabrani.  Towards Explainable Graph Representations in Digital Pathology [paper] [slideslive]
Paper 29: Minxing Pang and Jesper Tegnér.  Representation Learning and Translation between the Mouse and Human Brain using a Deep Transformer Architecture [paper] [Live only]
Paper 44: Jacob C. Kimmel and David R. Kelley.  scNym: Semi-supervised adversarial neural networks for single cell classification [paper] [slideslive]

Spotlights & Posters

Paper 8: Neha Prasad, Karren Yang and Caroline Uhler.  Optimal Transport using GANs for Lineage Tracing [paper] [poster] [slideslive]
Paper 16: Lingfei Wang, Jacques Deguine and Ramnik Xavier.  Normalisr: inferring single-cell differential and co-expression with linear association testing [poster] [slideslive]
Paper 17: Kexin Huang.  scGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph [paper] [poster] [slideslive]
Paper 37: Leander Dony, Martin Koenig, David S. Fischer and Fabian J. Theis.  Variational autoencoders with flexible priors enable robust distribution learning on single-cell RNA sequencing data [paper] [poster] [slideslive]
Paper 41: Mohammad Lotfollahi, Leander Dony, Harshita Agarwala and Fabian Theis.  Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data [paper] [poster] [slideslive]
Paper 2: Kathleen Lois Foster and Alessandro Maria Selvitella.  Learning the locomotion behaviour of lizards transfers across environments [paper] [poster] [slideslive]
Paper 23: Jacob Schreiber, Yang Young Lu and William Stafford Noble.  Ledidi: Designing genome edits that induce functional activity [paper] [poster] [slideslive]
Paper 46: Nic Fishman, Avanti Shrikumar, Georgi Marinov and Anshul Kundaje.  Systematic characterization of generative models for de novo design of regulatory DNA [paper] [poster] [slideslive]
Paper 49: Alexander Karollus, Ziga Avsec and Julien Gagneur.  Predicting Mean Ribosome Load for 5’UTR of any length using Deep Learning [poster] [slideslive]
Paper 51: Pinar Demetci, Rebecca Santorella, Bjorn Sandstede, William Stafford Noble and Ritambhara Singh.  Gromov–Wasserstein Optimal Transport to Align Single-Cell Multi-Omics Data [paper] [poster] [slideslive]

Posters

Paper 35: Ramon Viñas, Tiago Azevedo, Eric Gamazon and Pietro Liò.  Gene Expression Imputation with Generative Adversarial Imputation Nets [paper] [poster]
Paper 5: Kyohei Koyama, Kotaro Kamiya and Shion Honda.  Cross Attentive Antibody-Antigen Interaction Prediction with Multi-task Learning [paper] [poster]
Paper 7: Michal Rozenwald, Aleksandra Galitsyna, Mikhail S. Gelfand and Ekaterina Khrameeva.  DNA folding features prediction with Recurrent Neural Networks using epigenetic data [paper] [poster]
Paper 18: John Halloran and David Rocke.  GPU-Accelerated SVM Learning for Extremely Fast Massive-Scale Proteomics Classification [paper] [poster]
Paper 47: Jun Cheng, Muhammed Hasan Çelik, Anshul Kundaje and Julien Gagneur.  MTSplice predicts effects of genetic variants on tissue-specific splicing [paper] [poster]
Paper 53: Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius and Maria Rodriguez Martinez.  PaccMann^RL on SARS-CoV-2: Designing antiviral candidates with conditional generative models [paper] [poster]
Paper 58: Mara Finkelstein, Avanti Shrikumar and Anshul Kundaje.  Look at the Loss: Towards Robust Detection of False Positive Feature Interactions Learned by Neural Networks on Genomic Data [paper] [poster]
Paper 66: Gherman Novakovsky, Manu Saraswat, Oriol Fornes and Wyeth Wasserman.  Biologically-relevant transfer learning improves transcription factor binding prediction [paper] [poster]
Paper 67: Tanishq Abraham, Andrew Shaw, Daniel O'Connor, Austin Todd and Richard Levenson.  Slide-free MUSE Microscopy to H&E Histology Modality Conversionvia Unpaired Image-to-Image Translation GAN Models [paper] [poster]
Paper 63: Tariq Daouda, Reda Chhaibi, Prudencio Tossou and Alexandra-Chloé Villani.  Auto-encoders with fibered latent spaces: A geometric approach to batch correction [paper] [poster]
Paper 9: Bastian Rieck, Tristan Yates, Guy Wolf, Nicholas Turk-Browne and Smita Krishnaswamy.  Topological Methods for fMRI Data [paper] [poster]
Paper 13: Seojin Bang and Heewook Lee.  Identification of Epitope-TCR Binding Using A Generative Adversarial Network Model [paper] [poster]
Paper 15: Kexin Huang, Tianfan Fu, Cao Xiao, Lucas Glass and Jimeng Sun.  DeepPurpose: a Deep Learning Based Drug Repurposing Toolkit [paper] [poster]
Paper 26: Edward Lee, Jiangdian Song, Hongmei Wang, Wei Zhang, Jimmy Zheng, Michelle Han, Jayne Seekins, Simon Wong, Kexue Deng and Kristen Yeom.  Using deep learning on chest CT to track COVID-19 patients [poster]
Paper 28: Larisa Morales-Soto, Juan P. Bernal-Tamayo, Robert Lehman, Balsamy Subash, Xabier Martinez-de-Morentin, Amaia Vilas-Zornoza, Patxi San-Martin, David Lara, Felipe Prosper, David Gomez-Cabrero, Narsis Kiani and Jesper Tegner.  Deriving Cell Type-Specific Directed Weighted Signed Regulatory Networks from Single-Cell RNA Sequencing Data [paper] [poster]
Paper 31: Younhun Kim, Sawal Acharya, Daniel Alfonsetti, Georg Gerber, Bonnie Berger and Travis Gibson.  ChronoStrain: Sequence quality and time aware strain tracking with shotgun metagenomic data [poster]
Paper 36: Mohammad Sadegh Akhondzadeh, Alireza Omidi, Zeinab Maleki, Kevin R. Coombes, Amanda E. Toland and Amir Asiaee.  Learning Cancer Progression Network from Mutation Allele Frequencies [paper] [poster]
Paper 34: Stephen Malina, Daniel Cizin and David Knowles. Determining causal interactions learned by genomic DL models with in silico mutagenesis and Mendelian randomization [poster]
Paper 38: Russell Kunes, Siyu He, Yang Xiao, Simon Tavare and David Knowles. Supervised Tumor Cell Subtype Identification via SCAN [paper] [poster]
Paper 48: Junil Kim, Simon Toftholm Jakobsen, Kedar Nath Natarajan and Kyoung Jae Won.  TENET: Gene network reconstruction using single cell transcriptomic data \\ reveals key factors for embryonic stem cell differentiation [paper] [poster]
Paper 60: Ehsaneddin Asgari, Philipp Muench, Till-Robin Lesker, Alice C. Mchardy and Mohammad R. K. Mofrad.  Data-driven Variable-length Segmentation of Biological Sequences: Applications in Proteomics and Metagenomics [paper] [poster]
Paper 68: Sisi Qu, Mengmeng Xu, Bernard Ghanem and Jesper Tegner.  Learning Heat Diffusion for Network Alignment [paper] [poster]
Paper 69: Aditya Jadhav and Manikandan Narayanan.  Identifying cross-tissue signaling between genes from biomedical literature [poster]

Highlights of Published Work

Paper 3: Sanjiv Dwivedi, Andreas Tjärnberg, Jesper Tegnér and Mika Gustafsson.  Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Autoencoder [paper] [slideslive]
Paper 4: Iman Deznabi, Büşra Arabacı, Mehmet Koyuturk and Oznur Tastan.  DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations [paper] [slideslive]
Paper 14: Christian Matek, Simone Schwarz, Karsten Spiekermann and Carsten Marr.  A publicly available database for developing machine learning applications to differentiate leukocytes and recognise malignant cells in peripheral blood [paper] [slideslive]
Paper 21: Serghei Mangul, Igor Mandric and Jeremy Rotman.  Profiling immunoglobulin repertoires across multiple human tissues using RNA Sequencing [paper] [slideslive]
Paper 22: Shibiao Wan, Junil Kim and Kyoung Jae Won.  Hyper-fast and accurate clustering of ultra-large-scale single-cell data with ensemble random projection [paper] [slideslive]
Paper 57: Nova Smedley, Suzie El-Saden and William Hsu.  Discovering and interpreting transcriptomic drivers of imaging traits using neural networks [paper] [slideslive]
Paper 6: Jacob Schreiber and William Stafford Noble.  Learning a latent representation of human genomics using Avocado [paper] [slideslive]