Drug-response pathway analysis methods based on network analysis

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We developed computational/statistical methods for inferring abstract network models from time-course or case-control gene expression data of drug-responses. We also developed a series of knowledge processing technologies for efficiently constructing and systematically validating dynamic models from the literature information on biomolecular interactions. Bayesian network and state space model are employed for the former and an extended Petri net is used for the latter.