A method and system for determining cancer network markers based on a probability model

Abstract

The invention discloses a method and a system for determining cancer network markers based on a probability model. The method comprises the following steps: utilizing a probability density function, converting gene expression data matrices of all normal samples and disease samples obtained into likelihood matrices, and constructing a normal sample distribution function according to the likelihoodmatrices of all normal samples; each element in the likelihood matrix of each disease sample is then brought into the normal sample distribution function, the set of significantly different genes foreach disease sample is determined, and the set of significantly different genes for each disease sample is mapped to proteins. Protein-protein interaction networks, and network markers for each disease sample are identified. The method or system provided by the invention can accurately and effectively acquire cancer network markers, and utilize the cancer network markers to carry out subtype classification of diseases so as to realize precise diagnosis and treatment of diseases.

Description

technical field [0001] The invention relates to the technical field of gene detection, in particular to a method and system for determining cancer network markers based on a probability model. Background technique [0002] Existing studies have shown that the occurrence and development of cancer is the result of the joint action of multiple genes. Due to the shortcomings of traditional gene expression profile data such as large noise, few samples, and imbalance between positive and negative samples, combining expression profile data with biological networks to determine cancer network markers has become a potential solution. At the same time, network markers have higher efficiency and stability than previous single-gene markers. Contents of the invention [0003] The present invention provides a method and system for determining cancer network markers based on a probability model, on the basis of considering the heterogeneity between samples and the differences in disease...

AI Technical Summary

Problems solved by technology

Due to the shortcomings of traditional gene expression profile data such as large noise, few samples, and imbalance between positive and negative samples, combining expression profile data with biological networks to determine cancer network markers has become a potential solution.

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