Method and system for recognizing disease related factor on basis of functional module

Abstract

The invention provides a method and a system for recognizing a disease related factor on the basis of a functional module. The method comprises the following steps: 1) recognizing the functional module which includes a transcription factor, a miRNA and a target gene on a human co-regulatory network; 2) acquiring a p-value value of a differential expression gene and confirming a disease related functional module according to the combination of the differential expression gene in the module; 3) taking the non-differential expression gene, the miRNA and the target gene in the disease related functional module as candidate disease related factors; and 4) calculating associated values of the candidate disease related factors and the disease and sequencing the candidate disease related factors according to the associated values. According to the method, various interaction relations are combined when the disease related factor is recognized; even under the condition of unknown transcription factor, miRNA and gene function, the method provided by the invention also can be used for forecasting and recognizing the disease related factor; the method provided by the invention is high in accuracy of forecasting the disease related factor.

Description

Technical field [0001] The invention belongs to the field of bioinformatics, and relates to a method and system for identifying disease-related factors based on functional modules. Background technique [0002] The identification of disease-related miRNAs, transcription factors, and genes is of significant significance for studying and revealing the pathogenesis of complex diseases at the molecular level. Sequencing technology has greatly promoted people's research on the functions of miRNAs, transcription factors, and genes. Gene information and gene expression profile arrays are increasingly used to assist in the analysis of complex diseases. The scientific community widely believes that the comprehensive molecular characteristics of key factors for patients, such as gene expression, are the key to studying the treatment of complex diseases. When cells face environmental pressure, they mainly produce the required proteins by changing gene expression. Therefore, it is very impo...

AI Technical Summary

Problems solved by technology

First, the proposed method combines less information and does not reflect the cooperative relationship between miRNAs, transcription factors and genes in the process of disease occurrence

In addition, existing methods are not suitable for predicting the relationship between miRNAs, transcription factors and genes with unknown functions and diseases

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