Accepted papers

Contributed talks & Poster presentation

Mika Sarkin Jain, Bryan He, Nastaran Neishaboori, Erik Nohr, Rohan Joshi, James Zou: Predicting Tumor Mutation Burden from Histopathology Images Using Multiscale Deep Learning (Paper 65)
Trung Ngo Trong, Juha Mehtonen, Gerardo González, Roger Kramer, Ville Hautamäki and Merja Heinäniemi: SISUA: SemI-SUpervised generative Autoencoder for single cell data (Paper 50)
Ali Oskooei, Matteo Manica, Roland Mathis and María Rodríguez Martínez: Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer (Paper 4)

Spotlights & Poster presentation

Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Saez-Rodriguez and Maria Rodriguez Martinez: Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders (Paper 1)
Zhenqin Wu, Daniel Serie, Gege Xu and James Zou: PB-Net: Automatic Peak Integration by Sequential Deep Learning for Multiple Reaction Monitoring (Paper 6)
Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li and Jimeng Sun: MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals (Paper 8)
Karren Yang and Caroline Uhler: Multi-Domain Translation by Learning Uncoupled Autoencoders (Paper 13)
Mijung Kim, Ho-Min Park, Jae Yoon Kim, Sofie Van Hoecke and Wesley De Neve: Towards Diagnosis of Rotator Cuff Tears in 3-D MRI Using 3-D Convolutional Neural Networks (Paper 14)
Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael Jordan and Nir Yosef: A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements (Paper 26)
Julien Martinelli, Jeremy Grignard, Sylvain Soliman and François Fages: A Statistical Learning Algorithm for Inferring Reaction Networks from Time Series Data (Paper 37)
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger and Lucy Colwell: Critiquing Protein Family Classification Models Using Sufficient Input Subsets (Paper 55)
Ruishan Liu, Sten Linnarsson and James Zou: Inferring Single-cell Trajectory, Pseudo-time and Gene Regulation Using RNA Velocity (Paper 63)
Nicasia Beebe-Wang, Safiye Celik, Pascal Sturmfels, Sara Mostafavi and Su-In Lee: MD-AD: Multi-task deep learning for Alzheimer's disease neuropathology (Paper 64)

Paper Highlights & Poster presentation

Matthew D. Brooks, Jacopo Cirrone, Angelo V. Pasquino, Jose M. Alvarez, Joseph Swift, Shipra Mittal, Che-Lun Juang, Kranthi Varala, Rodrigo A. Gutiérrez, Gabriel Krouk, Dennis Shasha and Gloria M. Coruzzi: Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions (Paper 2)
Marko Järvenpää, Mohamad R. Abdul Sater, Georgia K. Lagoudas, Paul C. Blainey, Loren G. Miller, James A. McKinnell, Susan S. Huang, Yonatan H. Grad and Pekka Marttinen: A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation (Paper 5)
Elior Rahmani, Regev Schweiger, Liat Shenhav, Theodora Wingert, Ira Hofer, Eilon Gabel, Eleazar Eskin and Eran Halperin: A Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference
Elior Rahmani, Regev Schweiger, Brooke Rhead, Lindsey Criswell, Lisa Barcellos, Eleazar Eskin, Saharon Rosset, Sriram Sankararaman and Eran Halperin: Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Juho Timonen, Niina Lietzen, Riitta Lahesmaa, Aki Vehtari and Harri Lähdesmäki: An additive Gaussian process regression model for interpretable probabilistic non-parametric analysis of longitudinal data (Paper 49)

Poster presentation

Vikash Singh and Pietro Lio: Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction (Paper 9)
Dan DeBlasio, Kwanho Kim and Carl Kingsford: More accurate transcript assembly via parameter advising (Paper 15)
Rachel C. W. Chan, Mickael Mendez, Jeffrey A. Bilmes, Maxwell W. Libbrecht and Michael M. Hoffman: Semi-supervised Learning in Segway (Paper 16)
Matthew Amodio and Smita Krishnaswamy: Generating and Aligning from Data Geometries with Generative Adversarial Networks (Paper 18)
An Zheng, Michael Lamkin, Hao Su and Melissa Gymrek: AgentBind: Profiling Context-specific Determinants of Transcription Factor Binding Affinity (Paper 19)
Mehrdad Bakhtiari and Vineet Bafna: A read classification method to improve identification of tandem repeat variations in human genome (Paper 27)
Shantao Li, Jure Leskovec and James Zou: DotplotCNN: Applying Dot Plot on DNA Sequence Thoroughly Profile Homology Features (Paper 67)
Vishal Agarwal, N. Jayanth Kumar Reddy and Ashish Anand: Unsupervised Representation Learning of DNA Sequences (Paper 23)
Zeyang Shen and Christopher Glass: Interpreting Spacing Constraints of Transcription Factor Motifs from Convolutional Neural Networks (Paper 33)
Peter Koo and Matthew Ploenzke: Improving Convolutional Network Interpretability with Exponential Activations (Paper 44)
Benjamin DeMeo and Bonnie Berger: An Unsupervised Learning Approach for Sketching Single-cell Data (Paper 52)
Joseph Janizek, Ayse B Dincer, Scott Lundberg, Kamila Naxerova and Su-In Lee: EXPRESS: Explainable Prediction of Anti-Cancer Drug Synergy (Paper 58)
Weiguang Mao, Dennis Kostka, Maziyar Baran Pouyan and Maria Chikina: Non-negative Independent Factor Analysis for single cell RNA-seq (Paper 60)
Joel Mathew, Shobeir Fakhraei and José Luis Ambite: Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping (Paper 62)
Jasper Zuallaert, Xiaoyong Pan, Yvan Saeys, Xi Wang and Wesley De Neve: Investigating the biological relevance in trained embedding representations of protein sequences (Paper 7)
Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott and Jean-Philippe Vert: epiGWAS: novel methods for epistasis detection in genome-wide association studies (Paper 10)
Bowen Chen, Neda Shokraneh Kenari, Habib Daneshpajouh, Kay C. Wiese and Maxwell W. Libbrecht: Continuous chromatin state feature annotation of the human epigenome (Paper 22)
Disi Ji, Preston Putzel, Yu Qian, Richard Scheuermann, Jack Bui, Huan-You Wang and Padhraic Smyth: Learning Discriminative Gating Representations for Cytometry Data (Paper 25)
Paul Bertin, Mohammad Hashir, Martin Weiss, Geneviève Boucher, Vincent Frappier and Joseph Paul Cohen: Analysis of Gene Interaction Graphs for Biasing Machine Learning Models (Paper 30)
Peter Koo, Sharon Qian, Gal Kaplun, Verena Volf and Dimitris Kalimeris: Robust Neural Networks are More Interpretable for Genomics (Paper 42)
Amir Asiaee, Zachary Abrams, Samantha Nakayiza, Deepa Sampath and Kevin Coombes: Explaining Gene Expression Using Twenty-One MicroRNAs (Paper 43)
Ayse B Dincer, Joseph D. Janizek, Safiye Celik, Naozumi Hiranuma, Kamila Naxerova and Su-In Lee: DeepProfile: Interpretable Deep Learning of Latent Variables from a Compendium of Expression Profiles for 18 Human Cancers (Paper 51)
Peter He, Gerard Glowacki and Alexis Gkantiragas: Unsupervised Representations of Pollen in Bright-Field Microscopy (Paper 54)
Justas Dauparas, Haobo Wang, Peter Koo, Mor Nitzan and Sergey Ovchinnikov: Unified framework for multivariate distributions in biological sequences (Paper 56)
Mihir Mongia, Benjamin Soudry, Arash Gholami Davoodi and Hosein Mohimani: Efficient Database Search in Mass Spectrometry Based Proteomics Based on Distribution Sensitive Hashing (Paper 57)
Soo Bin Kwon and Jason Ernst: Learning a Human-Mouse Functional Genomics Conservation Score (Paper 59)
Mansi Mane, Aniket Deshmukh and Adam Iliff: Head and Tail Localization of C. elegans (Paper 68)
Ava Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John Guttag and Sangeeta Bhatia: Image Segmentation of Liver Stage Malaria Infection with Spatial Uncertainty Sampling (Paper 34)
Adam Riesselman, Jung-Eun Shin, Aaron Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew Kruse and Debora Marks: Designing Optimized and Efficient Antibody Libraries Using Autoregressive Generative Models (Paper 31)
Zi Wang, Dali Wang, Chengcheng Li, Yichi Xu, Husheng Li and Zhirong Bao: Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning (Paper 38)
Andreea-Ioana Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò and Jian Tang: Drug-Drug Adverse Effect Prediction with Graph Co-Attention (Paper 48)
Nikhil Yadala, Jyotirmoy Sethy and Navin Gupta: Unsupervised Neural Network based Independent Component Analysis for Neuroimaging (Paper 53)
Zhenghao Chen, Ilya Soifer, Hugo Hilton, Leeat Keren and Vladimir Jojic: Modeling multiplexed images with Spatial-LDA reveals novel tissue microenvironments (Paper 32)