Gene Expression Analysis and Genetic Network Modeling

Patrik D'haeseleer, Shoudan Liang, & Roland Somogyi


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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