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Structure Learning for Inferring a Biological Pathway

By Charlie Vaske

Abstract

The utility of graphical models often comes from their ability to predict the value of variables in the network, either with or without evidence. These predictions are based on conditional independencies encoded into the underlying structure of the network. However, an equally interesting problem is to discover the conditional independencies that underlie a body of data, to discover the relations of variables of which we may know nothing. One approach to discovering dependencies in data is to search for a graphical model that best explains it. This structure learning problem is well suited to the problem of systems biology. Though we know most of the genes that act in cells, we have very limited knowledge of how they interact. Discovering an interaction link can easily consume a year of a graduate student’s life, if it’s successful at all. Systems biology aims to establish the interaction of genes in a high-throughput manner, elucidating the function of genes from within cells. The protein from a gene is active within a context: there are a few proteins that are responsible for expressing the gene, and the protein itself interacts with a few other proteins and compounds. Thus, graphical models are a natural fit for modeling the behavior of a cell. In the simplest case a graphical model could have variables for the expression of a gene, and the scope of a factors determines the contextual interactions of the genes

Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.212.2049
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