Feb 3, 2007

Inference of genetic regulatory networks with recurrent neural network models

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Rui XuDonald C Wunsch

Abstract

Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.

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Mentioned in this Paper

Neural Network Simulation
Gene Expression

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