Monday, Jan 9, 2006, 16:15-17:15
Schreiber 309
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Ziv Bar Joseph
Title:
Data integration for understanding dynamic systems in the cell
Abstract:
Dynamic systems, such as the cell cycle and immune response, play an
important role in many biological processes. Recent advances in
high-throughput experimental methods are enabling researchers to obtain a
global view of the temporal expression profiles of such systems. Using time
series expression data we were able to model some of these dynamic systems
in yeast. However, when moving from model organisms to humans we face many
new computational challenges. Human systems are more complex, their temporal
duration is longer and the data is often noisier. Using ideas from machine
learning and graphical models I will present algorithms that combine time
series data with additional datasets, which are often static, to address
issues ranging from experiment design to data analysis to pattern
recognition and modeling.