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AIC Seminar Series

Machine Learning and Systems Biology

Saso DzeroskiJozef Stefan Institute in Ljubljana, Slovenia[Home Page]

Notice:  Hosted by Peter Karp

Date:  Thursday, December 15th 2016 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

   Abstract

In systems biology, a flood of information is being produced through a variety of “-omics” approaches. Genomics, transcriptomics, proteomics and metabolomics, are devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type. Taking advantage of this wealth of information has become a conditio sine qua non for the practitioners of systems biology. Machine learning naturally appears as one of the main drivers of progress in this context.

The talk will briefly introduce two machine learning approaches that are highly relevant for applications in systems biology. These include methods for structured output prediction and methods for learning the full dynamics of biological networks, including both network structure and the kinetics of the corresponding reactions. Example applications will be presented, which include gene function prediction as well as reconstructing the dynamics of endocytosis (more specifically endosome maturation).

   Bio for Saso Dzeroski

Saso Dzeroski leads a twenty-strong research group at the Department of Knowledge Technologies of the Jozef Stefan Institute in Ljubljana, Slovenia. He is also a full professor at the Jozef Stefan International Postgraduate School in Ljubljana. His research focuses on developing data mining and machine learning methods for predicting structured outputs and automated modeling of dynamic systems - and their applications to environmental and life sciences.

He is a fellow of the European Association for Artificial Intelligence (formerly ECCAI) and a member of the European Academy (Academia Europaea). He has participated in many international research projects and coordinated two of them in the past: He currently leads the project MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data) and is one of the principal investigators in the Human Brain Project. He has organized many scientific events, including the International Conference on Machine Learning: He is currently general chair of the 2017 edition of ECML/PKDD, the premier European conference on Data Science.

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