Learning Gene Network Using Conditional Dependence

Gene network, conventionally, is learned by studying the pairwise correlation of the microarray expression profiles of different genes. This approach, however, is reported to be effective only for learning a small portion of the regulatory pairs due to the complexity of the gene regulatory system. In this paper, through studying the conditional dependence of the gene expression profiles, a new algorithm, conditional dependence learning algorithm, is proposed which considers three additional factors: (1) the collaboration among regulators; (2) the formation of regulatory complex; and (3) the variable time delay to learn the gene network. Experiments on both artificial and real-life gene expression datasets validate the goodness of the algorithm

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