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Biomarker identification method and system based on dynamic network entropy

A biomarker and dynamic network technology, applied in biological systems, biostatistics, bioinformatics, etc., can solve problems such as inability to obtain dynamic features, and achieve high accuracy, accurate and reliable sources.

Pending Publication Date: 2022-01-04
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, in the process of disease occurrence and development, there is a lot of information behind the dysregulation of related genes and signaling pathways. Many methods for measuring this information have the limitation of not being able to obtain dynamic characteristics

Method used

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  • Biomarker identification method and system based on dynamic network entropy
  • Biomarker identification method and system based on dynamic network entropy
  • Biomarker identification method and system based on dynamic network entropy

Examples

Experimental program
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Embodiment 1

[0059] This embodiment discloses a biomarker identification method based on dynamic network entropy, such as figure 1 shown, including:

[0060] S1: Obtain disease-related functional gene pathways and gene interactions, and generate gene network pathways;

[0061] S2: Obtain gene expression data corresponding to different stages of the disease for the genes in the gene network pathway;

[0062] S3: Based on the dynamic network entropy, for different stages of the disease, calculate the pathway entropy of each pathway in the gene network pathway;

[0063] S4: Through statistical analysis of the pathway entropy of each pathway in different stages of the disease, determine the pathways that have a marker effect on the disease.

[0064] Each step is described in detail below.

[0065] Step S1 specifically includes:

[0066] Step S101: Obtain disease-related functional gene pathways, such as: pathway 1 (gene a, gene b, ...), pathway 2 (gene a, gene m, ...); what needs to be exp...

Embodiment 2

[0107] The first embodiment above provides a method for identifying biomarkers based on dynamic network entropy, which realizes the identification of gene pathways that can identify diseases. As a specific application, this embodiment provides a diabetes diagnosis system based on biomarker identification based on dynamic network entropy.

[0108] The system specifically includes:

[0109] The gene pathway screening module is used to screen the pathways that have a marker effect on diabetes according to the biomarker identification method described in Example 1, and record them as candidate pathways; wherein, diabetes is divided into two stages: normal and disease;

[0110] The diagnostic model training module is used to obtain the gene expression data of the normal group and the disease group corresponding to the candidate pathway as the initial data set; based on the initial data set, the support vector machine model is trained to obtain the diagnostic model;

[0111] Specif...

Embodiment 3

[0115] The first embodiment above provides a method for identifying biomarkers based on dynamic network entropy, which realizes the identification of gene pathways that can identify diseases. As a specific application, this embodiment provides a liver cancer diagnosis system based on biomarker identification based on dynamic network entropy.

[0116] The system specifically includes:

[0117] The gene pathway screening module is used to screen the pathways that have a marker effect on liver cancer according to the biomarker identification method described in Example 1, and record them as candidate pathways; wherein, liver cancer is divided into normal and multiple disease stages;

[0118] The diagnostic model training module is used to obtain the gene expression data of all stages corresponding to the candidate pathway as an initial data set; train the support vector machine model according to the initial data sets of each two adjacent stages to obtain a multi-classification d...

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Abstract

The invention discloses a biomarker identification method and system based on dynamic network entropy, and the method comprises the following steps: obtaining a functional gene pathway related to a disease and interaction between genes, and generating a gene network pathway; acquiring gene expression data corresponding to different stages of the disease for the genes in the gene network pathway; based on the dynamic network entropy, for different stages of the disease, calculating the pathway entropy of each pathway in the gene network pathways; and performing statistical analysis on the channel entropy of each channel in different stages of the disease to determine a channel with an identification effect on the disease. According to the method, the gene network pathway is constructed, information measurement is carried out on pathways in different stages of the disease by adopting entropy, and the gene pathway biomarkers related to the disease progress can be accurately identified by analyzing the change of the entropy.

Description

technical field [0001] The invention belongs to the field of disease biomarker identification in biological information calculation, and in particular relates to a biomarker identification method and system based on dynamic network entropy. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In the medical field, it is of great significance to the current human health and development to continuously improve the certainty, predictability and controllability of medical processes such as disease prediction, prevention and control, diagnosis, and treatment. The dynamic mechanism of development appears to be particularly important. [0004] For example, diabetes, a common chronic metabolic disease, has a low prevalence awareness rate, and many asymptomatic patients have been diagnosed. As a complex disease, the pathogenesis of diabetes has not y...

Claims

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Application Information

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IPC IPC(8): G16B5/20G16B25/00G16B40/00G16H50/20G06K9/62
CPCG16B5/20G16B25/00G16B40/00G16H50/20G06F18/2411G06F18/2431
Inventor 刘治平沈忱曹怡王怡娟高瑞
Owner SHANDONG UNIV
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