Broad Area Colloquium for Artificial Intelligence,
The computational challenge is to extract new biological understanding from this wealth of data. In particular, to gain insight on the causal "structure" of the interactions between components in the systems, such as genes, proteins, and external signals. In this talk, I will describe an ongoing project that addresses this question using probabilistic graphical models. These models represent the measured expression level of each gene as a random variable and each regulatory interaction as a probabilistic dependency between these variables. In this project, we developed tools for automatic induction of networks from gene expression data, and more importantly, for analysis of the confidence in different features of these networks.
This analysis allows us to examine various aspects of gene interaction: In the interaction between genes direct, or mediated by other genes? Is there a causal direction in the interaction? Are there common factors that might determine co-expression? Although these questions cannot, in general, be completely determined by gene expression profiles alone, we show that we can construct partial answers. We demonstrate the utility of this approach in analysis of yeast expression data sets. In particular, our method manages to reconstruct fragments of known pathways from such expression profiles.
This is joint work with Dana Pe'er, Iftach Nachman, Michal Linial, Gal Elidan, and Aviv Regev.
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