Machine Learning in Computational Biology
Oliver Stegle · Sara Mostafavi · Anna Goldenberg · Su-In Lee · Michael Leung · Anshul Kundaje · Mark B Gerstein · Martin Renqiang Min · Hannes Bretschneider · Francesco Paolo Casale · Loïc Schwaller · Amit G Deshwar · Benjamin A Logsdon · Yuanyang Zhang · Ali Punjani · Derek C Aguiar · Samuel Kaski

Sat Dec 13th 08:30 AM -- 06:30 PM @ Level 5, room 510 b
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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 data are high-dimensional, heterogeneous, and are impacted by a range of confounding factors, presenting new challenges for standard learning and inference approaches. Therefore, fully realizing the scientific and clinical potential of these data requires development of 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 goal of this workshop is to present emerging problems and innovative machine learning techniques in computational biology. We will invite several speakers from the biology/bioinformatics community who will present current research problems in computational biology. 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 to applications in personalized medicine, as illustrated by our invited speakers. 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.

Author Information

Oliver Stegle (EMBL-EBI)
Sara Mostafavi (University of British Columbia)
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.

Su-In Lee (University of Washington)
Michael Leung (University of Toronto)
Anshul Kundaje (Stanford University)
Mark B Gerstein (Yale)
Martin Renqiang Min (NEC Labs America)
Hannes Bretschneider (Deep Genomics Inc.)
Francesco Paolo Casale (European Bioinformatics Institute)
Loïc Schwaller (AgroParisTech / INRA)
Amit G Deshwar (University of Toronto)
Benjamin A Logsdon (Sage Bionetworks)
Yuanyang Zhang (Google)
Ali Punjani (University of Toronto)
Derek C Aguiar (Princeton University)
Samuel Kaski (Aalto University)

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