A workshop at the Annual Conference on Neural Information Processing Systems.
Saturday Dec 9th 2017, Long Beach Convention Center, Long Beach, CA, USA

Important dates

Instructions for posters

The maximum poster dimensions are 2.5 feet wide, 4 feet tall. Please note: this is different than previous years.

Invited Speakers



8:55 – 9:00     Opening remarks

9:00 – 9:40     Invited Speaker 1 – Christina Leslie - Decoding immune cell states and dysfunction

9:40 – 9:55     Talk 1 -- Denoising scRNA-seq Data Using Deep Count Autoencoders (Gökcen Eraslan)
9:55 – 10:10    Talk 2 -- Fine Mapping of Chromatin Interactions via Neural Nets (Artur Jaroszewicz)
10:10 – 10:25   Talk 3 -- TF-MoDISco: Learning High-Quality, Non-Redundant Transcription Factor Binding Motifs Using Deep Learning (Avanti Shrikumar)

10:30 – 11:00   Coffee break + posters

11:00 –12:00    Spotlights

12:00 – 1:30    Lunch + Posters

1:30 – 2:00     Posters

2:00 – 2:40     Invited Speaker 2 – Eran Halperin - A new sparse PCA algorithm with guaranteed asymptotic properties and applications in methylation data

2:40 – 2:55     Talk 4 – Variational Bayes inference algorithm for causal multivariate mediation with linkage disequilibrium (Yongjin Park)
2:55 – 3:10     Talk 5 – Reference-Free Archaic Admixture Segmentation Using A Permutation-Equivariant Network (Jeffrey Chan)
3:10 – 3:25     Talk 6 -- Robust and Scalable Models of Microbiome Dynamics for Bacteriotherapy Design (Travis Gibson)
3:30 – 4:00     Coffee break + posters

4:00 – 4:40     Invited Speaker 3 – Ben Raphael - Inferring Tumor Evolution

4:40 – 4:55     Talk 7 -- Drug Response Variational Autoencoder (Ladislav Rampášek)
4:55 – 5:10     Talk 8 -- Variational auto-encoding of protein sequences (Sam Sinai)
5:10 – 5:25     Talk 9 -- Antigen Identification for Cancer Immunotherapy by Deep Learning on Tumor HLA Peptides 

5:25 – 5:35     Sponsors + prizes


The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput technologies developed in the last decade now enable us to measure parts of a biological system at various resolutions—at the genome, epigenome, transcriptome, and proteome levels. These technologies are now being used to collect data for an ever-increasingly diverse set of problems, ranging from classical problems such as predicting differentially regulated genes between time points and predicting subcellular localization of RNA and proteins, to models that explore complex mechanistic hypotheses bridging the gap between genetics and disease, population genetics and transcriptional regulation. Fully realizing the scientific and clinical potential of these data requires developing novel supervised and unsupervised learning methods that are scalable, can accommodate heterogeneity, are robust to systematic noise and confounding factors, and provide mechanistic insights.

The goals of this workshop are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion on how to best model the intricacies of biological data and synthesize and interpret results in light of the current work in the field. We will invite several leaders at the intersection of computational biology and machine learning who will present current research problems in computational biology and lead these discussions based on their own research and experiences. We will also have the usual rigorous screening of contributed talks on novel learning approaches in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from established alternatives. Deep learning, kernel methods, graphical models, feature selection, non-parametric models and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. We will also encourage contributions to address new challenges in analyzing data generated from gene editing, single cell genomics and other novel technologies. The targeted audience are people with interest in machine learning and applications to relevant problems from the life sciences, including NIPS participants without any existing research link to computational biology. Many of the talks will be of interest to the broad machine learning community.


Program Committee

Jean-Philippe Vert (Ecole des Mines de Paris)
Karen Sachs (Stanford University)
Li Shen (Icahn School of Medicine at Mount Sinai)
Benjamin (Haibe-Kains)
Yun Song (University of California, Berkeley)
Laurent Jacob (Mines ParisTech)
Jian Ma (Carnegie Mellon University)
Yves Moreau (Katholieke Universiteit Leuven)
Andrew Delong (University of Toronto)
Sushmita Roy (University of Wisconsin-Madison)
Jinbo Xu Toyota (Technological Institute at Chicago)
Christina Leslie (Memorial Sloan-Kettering Cancer Center)
Nico Pfeifer (University of Tübingen)
Karsten Borgwardt (ETH Zurich)
Jason Ernst (University of California, Los Angeles)
Maxwell Libbrecht (University of Washington Genome Sciences)
Recep Colak (AWS)
Juho Rousu (Aalto University)
William Stafford Noble (University of Washington)
Leopold Parts (Wellcome Trust Sanger Institute)
Mathieu Blanchette (McGill University)
Alexander Schliep (Gothenburg University)
Claassen Manfred (ETH Zurich)
Martin Renqiang Min (NEC Laboratories America)
Antti Honkela (University of Helsinki)
Maria Chikina (Mount Sinai School of Medicine)
Oliver Stegle (EMBL-European Bioinformatics Institute)
Cedric Chauve (Simon Fraser University)
Gunnar Ratsch (Memorial Sloan Kettering Center)
Michael M. Hoffman (Princess Margaret Cancer Centre/University of Toronto)
Casey Greene (University of Pennsylvania)
Anna Goldenberg (University of Toronto)
Simon Rogers (Department of Computing Science, University of Glasgow)
Pierre Geurts (University of Liège)
Quaid Morris (University of Toronto)
Alexis Battle (Johns Hopkins University)
Chao Cheng (Dartmouth Medical School)
Guido Sanguinetti (The University of Edinburgh)
Bernard Ng (The University of British Columbia)
Michael A. Beer (Johns Hopkins University)
Su-In Lee (University of Washington)
Anthony Gitter (University of Wisconsin-Madison)