Machine Learning Approaches to Systems Biology
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Date: 04-13-2005
Start Time:
2:00pm
End Time: 3:00pm
Speaker: Chris Wiggins
From:
Applied Math, Columbia University
Location: CEPSR Interschool Lab
Hosted by:
Distributed Network Analysis (DNA) Lab
"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.