Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
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It is difficult to build a gene regulatory network directly. So the main interest focuses on the gene-gene regulation relationship mining, which reveals an active or repressive action from one gene to another. The previous methods, such as Event Method,Edge Detection Method,q-cluster method, didn't solve the gene regulatory relationship with a great succeed. In this paper, we propose a new method by introducing several more relational techniques. The results demonstrate the complete and detailed information between the genes.The data set and software will be available upon request.