Workshop
Machine Learning in Computational Biology
Nicolo Fusi · Anna Goldenberg · Sara Mostafavi · Gerald Quon · Oliver Stegle

Sat Dec 12th 08:30 AM -- 06:30 PM @ 510 bd
Event URL: http://www.mlcb.org/ »
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. <br><br>       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 rising leaders from the biology/bioinformatics community 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. 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 are particularly keen on considering contributions related to the prediction of functions from genotypes and that target data generated from novel technologies such as gene editing and single cell genomics, though we will consider all submissions that highlight applications of machine learning into computational biology. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences, including NIPS participants without any existing research link to computational biology.
09:00 AM Learning Deep Biological Architectures for Genomic Medicine (Talk) Brendan J Frey
09:45 AM Multi-Tac deep neural network to predict CpG methylation profiles from low-coverage sequencing data (Talk) Christof Angermueller
10:05 AM Large-Scale Sentence Clustering from Electronic Health Records for Genetic Associations in Cancer (Talk) Stefan Stark
10:10 AM Classifying Microscopy Images Using Convolutional Multiple Instance Learning (Talk) Oren Kraus
10:30 AM Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks (Talk) David Kelley
10:50 AM A probabilistic method for quantifying chromatin interactions (Talk) Henrik Mannerström
11:35 AM Detecting significant higher-order associations between genotype and phenotype while conditioning on covariates (Talk) Laetitia Papaxanthos
11:40 AM Genome-wide modelling of transcription kinetics reveals patterns of RNA production delays (Talk) Antti Honkela
11:45 AM Tensor decomposition and causal inference for multi-tissue gene expression experiments. (Talk) Victoria Hore
11:50 AM Disease mechanism discovery by integrating exome and gene expression datasets in one graphical model of disease (Talk) Aziz M Mezlini
02:30 PM Human Traits and Diseases (Talk) Dana Peer
03:15 PM Bayesian Gaussian Process latent variable models for pseudotime inference in single-cell RNA-seq data (Talk) Kieran Campbell
03:35 PM In Silico Design of Synthetic Genes for Total Cell Translation Control: a Multi-output Gaussian Processes approach (Talk) Javier Gonzalez

Author Information

Nicolo Fusi (Microsoft Research)
Anna Goldenberg (SickKids/University of Toronto)

Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Sara Mostafavi (University of British Columbia)
Gerald Quon (University of California, Davis)
Oliver Stegle (EMBL-EBI)

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