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Cancer driver gene prediction method and system based on local and global network centrality analysis

A technology of driving genes and prediction methods, applied in the fields of genomics, instrumentation, proteomics, etc., to achieve the effect of improving accuracy

Pending Publication Date: 2021-10-08
ANHUI UNIVERSITY
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Problems solved by technology

In addition, the method of identifying key nodes in the network based on network centrality ignores the attribute characteristics of gene nodes, the topological influence between neighbors, the influence of prior knowledge, and the attributes of the network itself.

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  • Cancer driver gene prediction method and system based on local and global network centrality analysis
  • Cancer driver gene prediction method and system based on local and global network centrality analysis
  • Cancer driver gene prediction method and system based on local and global network centrality analysis

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Embodiment Construction

[0049] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.

[0050] see figure 1 , the embodiment of the present invention includes:

[0051] A cancer driver gene prediction method based on local and global network centrality analysis, comprising the following steps:

[0052] S1: Preprocess the standardized somatic mutation data and gene expression data and express them in the form of gene-matrix;

[0053] Preferably, normalized somatic mutation data and gene expression data are downloaded from The Cancer Genome Atlas (TCGA) database. Further, the somatic mutation data include single nucleotide variations (SNVs) and chromosome aberrations (CNVs). Get validated cancer genes as positive samples (i.e. driver...

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Abstract

The invention discloses a cancer driver gene prediction method based on local and global network centrality analysis. The method comprises the following steps: S1, carrying out preprocessing of standardized somatic mutation data and gene expression data, and expressing the data into a gene-matrix form; S2, weighting a downloaded PPI network by using the preprocessed data; S3, constructing a model, and analyzing global and local features of the network by using an improved restart random walk algorithm; and S4, predicting a cancer data set by using the constructed model to obtain a ranking vector of a driver gene so as to realize prediction of the cancer driver gene. The invention further discloses a cancer driver gene prediction system based on local and global network centrality analysis. According to the invention, the driver gene can be better identified, the prediction precision of the cancer driver gene is greatly improved, and contributions are made to cancer diagnosis and development of precise medical treatment.

Description

technical field [0001] The invention relates to the field of biological information computing, in particular to a cancer driver gene prediction method and system based on local and global network centrality analysis. Background technique [0002] As one of the most deadly diseases in the world, the pathogenic mechanism of cancer is complex, and human beings have been continuously studying it until now. It is generally believed that cancer is caused by some somatic mutations, some of which confer cell growth and positive selection advantages , causing strong proliferation and tumors. In addition, the vast majority of somatic cell mutations are neutral or cause apoptosis and do not affect the occurrence and development of cancer and will not transform into cancer cells, so distinguishing which mutations play a role in the occurrence and development of tumor patients is the current cancer One of the main goals of treatment. Based on this goal, many algorithms for identifying ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B25/10G16B20/50G16B45/00
CPCG16B25/10G16B20/50G16B45/00
Inventor 郑春厚唐运运曹瑞芬夏俊峰苏延森
Owner ANHUI UNIVERSITY
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