Ron Shamir - COLLOQUIUM LECTURE
Supervised and unsupervised methods have been used extensively to
analyze genomics data, with mixed results. On one hand, new insights
have led to new biological findings. On the other hand, analysis results
were often not robust. Here we take a look at several such challenges
from the perspectives of networks and big data. Specifically, we ask if
and how the added information from a biological network helps in these
challenges. We show both examples where the network added information is
invaluable, and others where it is questionable. We also show that by
collectively analyzing omic data across multiple studies of many
diseases, robustness greatly improves.
Prof. Ron Shamir leads the Computational Genomics group at the Blavatnik
School of Computer Science, Tel Aviv University (TAU). He is the founder
and head of the Edmond J. Safra Center for Bioinformatics at TAU and
holds the Raymond and Beverly Sackler Chair in Bioinformatics. He
develops algorithmic methods in Bioinformatics and Systems Biology. His
research interests include gene expression analysis, molecular networks,
gene regulation and cancer genomics. Methods and software tools
developed by Shamir's group are in use by hundreds of
laboratories around the world.
Shamir received a BSc in Mathematics and Physics from the Hebrew
University, and a PhD in Operations Research from UC Berkeley in 1984.
He is on the faculty of TAU since 1987. He has published some 270
scientific works, including 17 books and edited volumes, and has
supervised more than 50 graduate students. He is on the editorial board
of eleven scientific journals and series, and was on the steering
committee of RECOMB. He co-founded the Israeli Society of Bioinformatics
and Computational Biology, and was society president in 2004-2006. He
is a recipient of the 2011 Landau Prize in Bioinformatics, and a Fellow
of the ISCB and the ACM.
Refreshments will be served from 14:15
Lecture starts at 14:30