Gene Expression Analysis and Genetic Network Modeling
Patrik D'haeseleer, Shoudan Liang, & Roland Somogyi
Description
Genomic technologies are enabling global views of the biomolecular
activity patterns underlying complex biological processes ranging from
cell division to differentiation and disease etiologies. The challenges
lie in a) inferring important functional relationships from these data,
b) effectively communicating these results using graphic tools, and c)
in designing experiments around the computational analysis strategies
which will lend meaning to the data. This tutorial will explore present
computational inference and visualization approaches based on the
analysis of experimental and model data. We will consider tools based on
multivariate statistics, information theory and discrete network models
as they apply to clustering, cross-classification and predictive
modeling.
Tutorial Notes
The complete tutorial notes for the gene
expression tutorial are available on the web in PDF format. Click here for
a free PDF
reader.
Biographical Sketches
Patrik D'haeseleer is a computer scientist conducting Ph.D. research at
the University of New Mexico, Dept. of Computer Science. He is advancing
multivariate statistical approaches to gene expression data.
Shoudan Liang, Ph.D., is a scientist at the NASA/Ames research center,
SETI program, and a data analysis consultant at Incyte Pharmaceuticals,
Inc. He has conducted extensive research on the global behavior of
Boolean genetic network models, and has developed reverse engineering
and fast clustering algorithms for application to large-scale gene
expression data.
Roland Somogyi, Ph.D., is director of Neurobiology at Incyte
Pharmaceuticals, Inc. His group has pioneered experimental technologies
for high-throughput gene expression analysis in CNS research. In a
cross-disciplinary collaboration with other researchers, he has pursued
novel computational inference and visualization approaches for the
interpretation of these data.
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