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Abstract:
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Biology networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this talk, I will introduce a new method for incorporating prior network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for the disease phenotypes of interest, and at the same time identifying disease related genes. We proposed to employ the gradient descent boosting procedure to build an additive tree model. In this model, each small tree weak learner is built by specifically utilizing the topology of the prior biological network. The performance of the method will be illustrated through both simulation studies and one real application.
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