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Machine Learning in Computational Biology
James Zou · Anshul Kundaje · Gerald Quon · Nicolo Fusi · Sara Mostafavi

Sat Dec 09 08:00 AM -- 06:30 PM (PST) @ 104 B
Event URL: https://mlcb.github.io/ »

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.

Sat 8:55 a.m. - 9:00 a.m.
Opening Remarks
Sat 9:00 a.m. - 9:40 a.m.
Christina Leslie - Decoding immune cell states and dysfunction (Invited Speaker)
Sat 9:40 a.m. - 9:55 a.m.
Denoising scRNA-seq Data Using Deep Count Autoencoders (Gökcen Eraslan) (Talk)
Sat 9:55 a.m. - 10:10 a.m.
Fine Mapping of Chromatin Interactions via Neural Nets (Artur Jaroszewicz) (Talk)
Sat 10:10 a.m. - 10:25 a.m.
F-MoDISco: Learning High-Quality, Non-Redundant Transcription Factor Binding Motifs Using Deep Learning (Avanti Shrikumar) (Talk)
Sat 10:30 a.m. - 11:00 a.m.
Coffee break + posters
Sat 11:00 a.m. - 12:00 p.m.
Spotlights (Spotllights)
Sat 12:00 p.m. - 1:30 p.m.
Lunch+Posters (Posters)
Sat 1:30 p.m. - 2:00 p.m.
Sat 2:00 p.m. - 2:40 p.m.
Eran Halperin - A new sparse PCA algorithm with guaranteed asymptotic properties and applications in methylation data (Invited Speaker)
Sat 2:40 p.m. - 2:55 p.m.
Variational Bayes inference algorithm for causal multivariate mediation with linkage disequilibrium (Yongjin Park) (Talk)
Sat 2:55 p.m. - 3:10 p.m.
Reference-Free Archaic Admixture Segmentation Using A Permutation-Equivariant Network (Jeffrey Chan) (Talk)
Sat 3:10 p.m. - 3:25 p.m.
Robust and Scalable Models of Microbiome Dynamics for Bacteriotherapy Design (Travis Gibson) (Takl)
Sat 4:00 p.m. - 4:40 p.m.
Ben Raphael - Inferring Tumor Evolution (Invited Speaker)
Sat 4:40 p.m. - 4:55 p.m.
Drug Response Variational Autoencoder (Ladislav Rampášek) (Talk)
Sat 4:55 p.m. - 5:10 p.m.
Variational auto-encoding of protein sequences (Sam Sinai) (Talk)
Sat 5:10 p.m. - 5:25 p.m.
Antigen Identification for Cancer Immunotherapy by Deep Learning on Tumor HLA Peptides (Talk)
Sat 5:25 p.m. - 5:35 p.m.
Sponsors + prizes
Sat 6:00 p.m. - 6:05 p.m.
Test push to whova (test)

Author Information

James Zou (Stanford)
Anshul Kundaje (Stanford University)
Gerald Quon (University of California, Davis)
Nicolo Fusi (Microsoft Research)
Sara Mostafavi (University of British Columbia)

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