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)