Dana Pe'er - Disseration

From Gene Expression to Molecular Pathways

Molecular networks involving interacting proteins, RNA, and DNA molecules, underlie the major functions of living cells. DNA microarrays probe how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. In this dissertation we address the challenge of reconstructing molecular pathways and gene regulation from gene expression data. Our goal is to automatically infer regulatory relations between genes, as well as other types of molecular interactions. To answer this challenge, we develop probabilistic graphical models of the biological system. We offer three such models and algorithms to automatically learn these from gene expression data. Our models and learning algorithms are based on the assumption that statistical correlation might indicate molecular or genetic interaction. We offer systematic evaluation for each of the methods presented culminating in experimental validation of novel predictions, automatically generated by one of our models.

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