Early pathogenic factor detection method based on gene co-expression network propagation analysis

Abstract

The invention provides an early-stage pathogenic factor detection method based on gene co-expression network spreading analysis. The method comprises the following steps that: 1) obtaining data from the true gene expression of the case group and the control group of a certain disease, and preprocessing the gene expression data of a certain disease; 2) through correlation analysis, determining whether two genes have a coexpression relationship or not so as to establish a gene coexpression network; 3) finding a fault node set from the differential expression soring of the gene; and 4) calculating an average distance of the nodes of a fault node subset and a prepared fault spreading center, finding an incremental average distance sequence, and determining a dynamic network biomarker. The method aims at the detection problem of the early-stage pathogenic factor of a complex disease to put forward the early-stage pathogenic factor detection method based on gene co-expression network spreading analysis from the perspective of a system in order to make up the deficiencies of a traditional molecular biomarker and a statistic network biomarker, the early-stage pathogenic factor of a disease can be effectively found, and a contribution is made for accurate medical treatment.

Description

technical field [0001] The present invention provides a method for detecting early pathogenic factors based on gene co-expression network propagation analysis, which involves a method based on gene overexpression (considered as a fault in gene expression) in a gene co-expression network (gene co-expression network) The detection method of early pathogenic factors in the transmission mechanism is an interdisciplinary technical field of complex network dynamics and biomedicine. Background technique [0002] Previous studies have shown that the development of diseases is usually accompanied by drastic changes, and the system undergoes a qualitative transition from a normal state to a diseased state. Once a system moves from a critical state to a diseased state, it is very difficult to reverse to the previous normal state, even with advanced medical treatments. Therefore, the detection of early pathogenic factors of diseases is very important. The prevention and treatment of d...

AI Technical Summary

Problems solved by technology

[0007] In summary, although the existing gene network analysis has a certain effect on understanding and predicting pathogenic factors, most of these static network markers do not consider the dynamic characteristics of the system

Images