Gene module mining method and device based on graph neural network, and computer equipment

Abstract

The invention discloses a gene module mining method and device based on a graph neural network, and computer equipment. The method comprises the following steps: constructing a gene co-expression network according to gene expression profile data, configuring a community membership matrix in a graph neural network mode based on the constructed gene co-expression network, and generating a known module in a threshold setting mode based on the configured community membership matrix. By means of the mode, the community affiliation matrix can be configured in a graph neural network representation learning mode, then the known module is generated in a threshold setting mode, and it is achieved that multiple genes with dense connection can be allowed to belong to different modules on the gene module mining result.

Description

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AI Technical Summary

Benefits of technology

The present technology describes an algorithm for discovering communities within networks called social relationships or groupings (GWs). It uses mathematical models from biology-related fields such as neuroscience and computer science to predict how similar individuals are likely connected together more frequently than they would otherwise have if their parents were directly related. This helps researchers better identify groups containing diverse types of people who share common characteristics like race gender age smoking habits or environmental factors affecting them.

Problems solved by technology

This patents describes techniques called cluster analysis, where groups of DNA segments from one type of cell undergo complex interactions during their lifecycle. It involves dividing them into smaller parts like chromosoms based upon factors like structure and function within each part. Cluster analysis allows scientists to identify specific areas containing important components without having to manually analyze entire databases due to its ability to group together diverse structures across generations quickly.

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