Contributed talks & Poster presentation

Attentive cross-modal paratope prediction, Andreea Deac, Petar Veličković and Pietro Sormanni

Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data, Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson and Percy Liang

scLL-LVM: Bayesian Manifold Learning for Single-Cell Gene Expression Data, Cassandra Burdziak, Mervin Fansler and Dana Pe'er

DECODER: A probabilistic approach to integrate big data reveals mitochondrial Complex I as a potential Alzheimer’s disease therapeutic target, Safiye Celik, Josh C Russell, Cezar R Pestana, Ting-I Lee, Shubhabrata Mukherjee, Paul K Crane, Dirk Keene, Jennifer F Bobb, Matt Kaeberlein and Su-In Lee

Spotlights & Poster presentation

Regularization Learning Networks, Ira Shavitt and Eran Segal

Compositional modeling of core splicing regulation improves prediction of variant effects on splicing, Jun Cheng, Nguyen Thi Yen Duong, Ziga Avsec and Julien Gagneur

Auto-Encoding Topographic Factors, Antonio Moretti, Andrew Stirn and Itsik Pe'er

Continuous embeddings of DNA sequencing reads and application to metagenomics, Menegaux Romain and Jean-Philippe Vert

Toward an Alignment-Free Method for Feature Extraction and Accurate Classification of Viral Sequences, Dylan Lebatteux, Mohamed Amine Remita and Abdoulaye Baniré Diallo

MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples, Ehsaneddin Asgari, Kiavash Garakani, Alice McHardy and Mohammad Mofrad

Linkage-Disequilibrium Regularized Support Vector Machines for Genome-Wide Association Studies, Mukund Sudarshan and Lakshmi Subramanian

Poster presentation

Feedback GAN for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions, Anvita Gupta and James Zou

High-throughput Phage Screening to Predict Pathogenicity of E.coli strains, Tatiana Lenskaia and Daniel Boley

Graph Capsule Convolutional Neural Networks, Saurabh Verma and Zhi-Li Zhang

Unravelling the spectrum of bi-locus diseases, Aziz Fouché, Nassim Versbraegen, Charlotte Nachtegael, Sofia Papadimitriou, Andrea Gazzo, Guillaume Smits and Tom Lenaerts

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data, Hamid Eghbal-Zadeh, Lukas Fischer, Niko Popitsch, Florian Kromp, Sabine Taschner-Mandl, Khaled Koutini, Teresa Gerber, Eva Bozsaky, Peter Ambros, Inge Ambros, Gerhard Widmer and Bernhard A. Moser

Diabetic Retinopathy Grading using Deep Siamese Network, Anisha Gunjal

Protein Space Embedding Kernel for Plaque Volume Prediction, João Pereira and Evgeni Levin

Batch Reinforcement Learning Stochastic Policy to shift Basin of Attractions in Partially Observable Biological Systems, Cyntia E. H. Nishida, Reinaldo A. C. Bianchi and Anna Helena Reali Costa

A multi-level Bayesian nonparametric model of longitudinal responses of the human microbiota to dietary interventions, Richard Creswell, Molly K. Gibson, Travis E. Gibson, Jonathan W. Leff and Georg K. Gerber

Neuron Interference: Evidence-Based Batch Effect Removal, Matthew Amodio, Ruth Montgomery, Jenna Pappalardo, David Hafler and Smita Krishnaswamy

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases, C. H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M.D. and Jesper Tegner

Geometry Based Data Generation, Ofir Lindenbaum, Jay Stanley Iii, Guy Wolf and Smita Krishnaswamy

Kipoi: accelerating the community exchange and reuse of predictive models for regulatory genomics, Žiga Avsec, Roman Kreuzhuber, Johnny Israeli, Jun Cheng, Lara Urban, Abhimanyu Banerjee, Nancy Xu, Avanti Shrikumar, Willem H. Ouwehand, Anshul Kundaje, Oliver Stegle and Julien Gagneur

Deep reinforcement learning and simulation as a path toward precision medicine, Brenden Petersen, Jiachen Yang, Will Grathwohl, Chase Cockrell, Claudio Santiago, Gary An and Daniel Faissol

Optimally Searching for Cancer Genes Using Submodular Models, Gideon Dresdner, Sebastian Tschiatschek, Viktor Gál and Gunnar Rätsch

Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko and Yoshua Bengio

A Sequence-to-sequence Regression of Genome-wide Chromatin Data through Adversarial Training, Jesik Min, Johnny Israeli and Anshul Kundaje

Modeling Dynamics with Deep Transition-Learning Networks, David van Dijk, Scott Gigante, Kevin R. Moon, Alexander Strzalkowski, Katie Ferguson, Jessica Cardin, Guy Wolf and Smita Krishnaswamy

Variational Homolog Encoder, Tomasz Blazejewski and Harris Wang

Learning Functions in Large Networks requires Modularity and produces Multi-Agent Dynamics, C. H. Huck Yang, Rise Ooi, Tom Hiscock, Vctor Eguluz and Jesper Tegner

Feature Selection and Case-based Reasoning for Survival Analysis in Bioinformatics, Isabelle Bichindaritz, Charles Enblebert, Angelina Regua and Leszek Kotula

MERGE-Combo: Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine, Joseph Janizek, Safiye Celik and Su-In Lee

Efficiently mining recurrent substructures from protein 3D-structure graphs, Rabie Saidi, Wajdi Dhifli, Mondher Maddouri and Engelbert Mephu Nguifo

DeepProfile: Deep learning of cancer molecular profiles for precision medicine, Ayse B Dincer, Safiye Celik, Naozumi Hiranuma and Su-In Lee

Protein Contact Network Analysis with Graph Neural Networks, Giacomo Meanti, Stefan Bauer, Tilman Flock, Xavier Deupi I Corral and Joachim Buhmann

Learning to rank for censored survival data, Margaux Luck, Tristan Sylvain, Joseph Paul Cohen, Heloise Cardinal, Andrea Lodi and Yoshua Bengio

Deep Multimodal Learning to Investigate Microbe-Molecule Interactions, James Morton, Alexander Aksenov, Brooke Anderson, Louis-Flix Nothias-Scaglia, Se Jin Song, Chris Callewaert, Pieter Dorrestein, Rachel Dutton and Rob Knight

MD-AD: Multi-task deep learning for Alzheimer's disease neuropathology, Nicasia Beebe-Wang, Safiye Celik and Su-In Lee

BPNet: Learning single-nucleotide resolution predictive models of in vivo transcription factor binding from ChIP-nexus data, Ziga Avsec, Johnny Israeli, Robin Fropf, Melanie Weilert, Julia Zeitlinger and Anshul Kundaje

Paper Highlights

Selective Classification via Curve Optimization, Avanti Shrikumar, Amr Alexandari and Anshul Kundaje

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants, Jacopo Cirrone*, Kranthi Varala*, Amy Marshall-Colón*, Matthew D. Brooks, Angelo V. Pasquino, Sophie Léran, Shipra Mittal, Tara M. Rock, Molly B. Edwards, Grace J. Kim, Sandrine Ruffel, W. Richard McCombie, Dennis Shasha and Gloria M. Coruzzi