Double clustering method for tumor gene expression profile data based on double hypergraph regularization

Abstract

The invention discloses a double-clustering method for tumor gene expression profile data based on double hypergraph regularization, by clustering the samples and genes of the tumor gene expression profile data respectively; then, the samples and genes of the tumor gene expression profile data Genes respectively establish sample hypergraph and gene hypergraph to obtain the inherent geometric structure of samples and genes; finally, the sample hypergraph and gene hypergraph are respectively used as the sample hypergraph regularization item and gene hypergraph regularization item of principal component analysis, and determine The objective function is optimized, and finally the sample clustering matrix and gene clustering matrix are respectively optimized by optimizing the objective function to obtain the final sample clustering and gene clustering. Based on the principle component analysis method, the present invention optimizes the double clustering through double hypergraph regularization, so as to better obtain the complex information in the tumor gene expression profile data on the basis of retaining the advantages of the principal component analysis method, Finally, the accuracy of clustering is improved.

Description

technical field [0001] The invention relates to a double clustering method for tumor gene expression profile data, in particular to a double hypergraph regularization based double clustering method for tumor gene expression profile data. Background technique [0002] So far, more than 100 different tumors have endangered human health. Sample types in tumor gene expression profiling data can be distinguished by the molecular patterns of gene activity in tumor cells. In recent years, with the rapid development of DNA microarray technology, researchers can observe the expression levels of thousands of genes at the same time, which can study tumor gene expression profile data more comprehensively. The current challenge of molecular biology is how to mine the important information contained in these tumor gene expression profile data to understand the biological process and mechanism of tumors. Due to the development of pattern recognition and machine learning, many effective m...

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Problems solved by technology

In this case, principal component analysis methods cannot mine the inherent geometric structure of real-world data

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