Title: Machine Learning Approaches to Systems Biology

Abstract:

"Systems Biology," the study of collective phenomena in molecular biology, is bo
th informed and constrained by an unprecedented volume of biological data and de
cades of prior biological knowledge. In this talk I will illustrate how these da
ta, along with tools from machine learning and information theory, can be exploi
ted for studying the inference, organization, and origin of biological networks.
These include supervised learning approaches for integration of sequence and DN
A microarray data; information theoretic approaches to network organization; and
discriminative classification for rendering network origins.