Machine Learning in Computational Biology
Gerald Quon · Sara Mostafavi · James Y Zou · Barbara Engelhardt · Oliver Stegle · Nicolo Fusi

Sat Dec 10th 08:00 AM -- 06:30 PM @ Room 212
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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.

08:35 AM Introduction (Talk)
08:40 AM TBA (Keynote) Jonathan Marchini
09:25 AM Multiple Output Regression with Latent Noise. (Talk) Jussi Gillberg
09:45 AM Predicting Protein Folding by Ultra-Deep Learning. (Talk) Jinbo Xu
10:05 AM Dissecting the non-infinitesimal architecture of complex traits using group spike-and-slab priors. (Talk) Abhishek Sarkar
11:00 AM Poster Session <span> <a href="#"></a> </span>
12:30 PM Lunch <span> <a href="#"></a> </span>
01:30 PM Predicting the impact of rare regulatory variation. (Keynote) Alexis Battle
02:15 PM Modelling-based experiment retrieval: A case study with gene expression clustering. (Talk) Paul Blomstedt
02:35 PM Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction. (Talk) Vaishaal Shankar
03:30 PM Modelling cell-cell interactions with spatial Gaussian processes. (Talk) Damien Arnol
03:50 PM Predicting off-target effects for CRISPR guide design. (Talk) Jennifer Listgarten
04:10 PM Beta Tucker decomposition for DNA methylation data. (Talk) Aaron Schein
04:30 PM Deep Learning for Branch Point Selection in RNA Splicing. (Talk) Victoria Dean
04:50 PM Applying Faster R-CNN for Object Detection on Malaria Images. (Talk) Jane Hung
05:10 PM Deep learning and new technologies in compbio. (Panel Discussion)
05:55 PM Closing (Talk)

Author Information

Gerald Quon (University of California, Davis)
Sara Mostafavi (University of British Columbia)
James Y Zou (Microsoft Research)
Barbara Engelhardt (Princeton University)
Oliver Stegle (EMBL-EBI)
Nicolo Fusi (Microsoft Research)

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