Additive noise model based gene regulatory network construction algorithm

Abstract:

In order to represent causal relationship when relevance measure is used in statistic inference methods to filter gene pair, inspired by the research that casual-effect orientation algorithm can identify direction of causal-effect variables effectively,we propose an additive noise model based on the gene regulatory network construction algorithm by using additive noise model orientation algorithm to measure degree of causal relationship. The algorithm extends additive noise model based orientation algorithm to a feature selective algorithm, and builds ANM model of transcription factors set and each gene to select transcription factors of gene. In the experiments of three datasets DREAM5, the method has clear improvement in comparison with other algorithms, and could be used as an efficient algorithm to build gene regulatory networks.