Constructing Gene Regulatory Network By Integrating Diverse Genomic Data

Constructing gene regulatory network is one of the most challenging issues in the post-genome research, the depth of which not only enriches our understanding of the mechanism of cellular processes and furthers the cause of disease, but also develops the treatment of disease and manufacture of genetic pharmacy. The study mainly focuses on the design of effective computational method, which is established from the enhancement of the accuracy and efficiency of algorithm in constructing the gene regulatory network. This article carries on the following several aspects.Firstly, inspired by the bottom-to-up network construct strategy, we conducted a study of the regulatory relationship between the TF-TG pair, based on the gene expression data. According to the characteristics of realistic problems, we devised a new classified algorithm, which was shown superior prediction precision over other mature classified methods. Furthermore, to reveal the mechanism of gene regulatory networks, a new method was firstly proposed to identify synergistic transcriptional factors by integrating gene expression data with ChIP-chip data. And then the cooperative TFs involved in the yeast cell cycle was analyzed. As a consequence, more than half of our predicted results were either confirmed by literature evidences or complied with the results announced by previous computational methods.Secondly, based on the framework of Bayesian Network and integration of diverse genomic data, we proposed a novel method to construct gene regulatory networks, which can avoid the disadvantages of traditional methods, and provide an effective solution for the construction of gene regulatory networks.