6533b872fe1ef96bd12d346c

RESEARCH PRODUCT

INFERRING GENE NETWORKS FROM MICROARRAY WITH GRAPHICAL MODELS

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ABSTRACT. Microarray technology allows to collect a large amount of genetic data, such as gene expression data. The activity of the genes are coordinate by a complex network that regulates their expressions controlling common functions, such as the formation of a transcriptional complex or the availability of a signalling pathway. Understanding this organization is crucial to explain normal cell physiology as well as to analyse complex pathological phenotypes. Graphical models are a class of statistical models that can be used to infer gene regulatory networks. In this paper, we examine a class of graphical models: the strongly decomposable graphical models for mixed variables. Among oth- ers properties, explicit expressions of maximum likelihood estimators are available for decomposable graphical models. This property makes the use of decomposable model suitable for high-dimensional data. We apply decomposable graphical models to a real dataset example.