Gene regulation and control network re-building method based on cross-platform gene expression data

Abstract

The invention relates to a gene regulation and control network re-building method based on cross-platform gene expression data. Gene expression data from p sequencing platforms is obtained and preprocessed according to the characteristics of the cross-platform gene expression data, n gene expression quantities are extracted from each gene sample, a father and son node sets of each gene expression quantity is obtained based on the mixed type condition independence testing of partial correlation coefficients, the father and son node sets are applied to the three processes of a learning network framework of cross-platform cause-effect network structure learning, v-structure determination and maximized direction marking, and a cross-platform gene regulation and control network is rebuilt. The problems of a cross-platform gene regulation and control network are solved through a cause-effect graph model, high-dimensional gene regulation and control network rebuilding can be directly and effectively carried out through the cross-platform gene expression data, data excess smoothness and other problems caused in the data preprocessing process are avoided, and the correctness rate and the recall rate of the rebuilding result of the cross-platform gene regulation and control network are increased.

Description

technical field [0001] The invention relates to the technical field of biological information processing, in particular to a gene regulation network reconstruction method based on cross-platform gene expression data. Background technique [0002] The gene regulatory network is a complex network that reflects the mutual influence relationship between genes. It plays a very important role in the study of the internal laws of life phenomena. It is a challenge in bioinformatics to use gene expression data to discover the gene regulatory relationship. With the rapid development of gene microarray technology, a variety of gene sequencing platforms have emerged. However, due to the different sequencing platforms and the differences in experimental environments, the gene expression data generated under different sequencing platforms often cannot be compared directly. , high-dimensional issues. Typical gene regulatory network reconstruction methods are mostly suitable for single-pla...

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Problems solved by technology

Therefore, the causal graph model can well solve the problem of gene regulatory network reconstruction. In recent years, there have been many studies on gene regulatory network reconstruction based on causal network structure learning methods. Experimental results of these methods applied to gene expression data of a single sequencing platform are also available. good performance

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