Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module

doi:10.1109/TCBB.2021.3114281

Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module

2022-11-01

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DOI：10.1109/TCBB.2021.3114281

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Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module detection into the problem of finding community structure in the graph, and introduce the latest network representation learning method-graph neural network to optimize this problem. In order to systematically evaluate whether the algorithm allows overlap to affect such problems, we make two variants of the output of the algorithm, Deepgmd_cluster and Deepgmd. The difference between them is whether overlap is allowed. By comparing the known modules and the modules generated by the algorithm, we can evaluate the quality of the algorithm. We use this method to compare our algorithm with some current mainstream methods. The results show that our method has greater advantages. In the end, we analyze some typical modules from the modules found by the algorithm for visualization, and use the GO database and KEGG database to perform enrichment analysis and pathway analysis on these modules.

Keywords:

Task analysis

Gene expression

Data mining

Standards

Clustering algorithms

Regulators

Visual databases

Gene expression profile

module mining

network representation learning

graph neural network

overlapped module

Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module

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