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Machine Learning in Computational Biology
Jean-Philippe Vert · Gunnar Rätsch · Yanjun Qi · Tomer Hertz · Anna Goldenberg · Christina Leslie

Fri Dec 16 10:30 PM -- 11:00 AM (PST) @ Melia Sierra Nevada: Genil
Event URL: http://mlcb.org »

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data are often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources. Furthermore, next generation sequencing technologies are yielding terabyte scale data sets that require novel algorithmic solutions.

The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. We invited several speakers from the biology/bioinformatics community who will present current research problems in bioinformatics, and we will invite 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 standard approaches. Kernel methods, graphical models, feature selection, and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.

Computational biology currently attracts great interest in the NIPS community, but there is still no yearly forum for advances in machine learning for computational biology within existing conferences in the two fields. Over the past few years, we have been working to establish this workshop as a recurring annual meeting in order to provide such a forum. In addition to having continuity among the organizers, we have enlisted a distinguished program committee to ensure that diverse work of the best quality is represented at the workshop. Typically, at least one invited speaker has been a prominent molecular biologist, with the goal of introducing the audience to emerging problems, technologies, and data sources from a biological viewpoint. We have previously organized BMC Bioinformatics special issues with work presented at the workshop, to increase the visibility of learning methods in computational biology. We have also attracted funding from the EU PASCAL2 network to support invited speakers and video recording of the talks for publication on http://videolectures.net.

Author Information

Jean-Philippe Vert (Google)
Gunnar Rätsch (ETH Zürich)
Yanjun Qi (University of Virginia)
Tomer Hertz (Fred Hutchnison Cancer Research Center)
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.

Christina Leslie (Memorial Sloan Kettering Cancer Center)

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