miRDM-rfGA: Genetic algorithm-based identification of a miRNA set for detecting type 2 diabetes

BMC Medical Genomics

Research

Aron Park¹  Seungyoon Nam² 

[1] Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, 21999, Incheon, Korea;Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, 21999, Incheon, Korea;Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon University Gil Medical Center, Gachon University College of Medicine, 21565, Incheon, Korea;

关键词: Genetic algorithm;    Feature selection;    miRNA;    Type 2 diabetes;    Biomarker discovery;   

DOI  :  10.1186/s12920-023-01636-2

 received in 2022-10-31, accepted in 2023-08-17,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundType 2 diabetes mellitus (T2DM) affects approximately 451 million adults globally. In this study, we identified the optimal combination of marker candidates for detecting T2DM using miRNA-Seq data from 95 samples including T2DM and healthy individuals.MethodsWe utilized the genetic algorithm (GA) in the discovery of an optimal miRNA biomarker set. We discovered miRNA subsets consisting of three miRNAs for detecting T2DM by random forest-based GA (miRDM-rfGA) as a feature selection algorithm and created six GA parameter settings and three settings using traditional feature selection methods (F-test and Lasso). We then evaluated the prediction performance to detect T2DM in the miRNA subsets derived from each setting.ResultsThe miRNA subset in setting 5 using miRDM-rfGA performed the best in detecting T2DM (mean AUROC = 0.92). Target mRNA identification and functional enrichment analysis of the best miRNA subset (hsa-miR-125b-5p, hsa-miR-7-5p, and hsa-let-7b-5p) validated that this combination was involved in T2DM. We also confirmed that the targeted genes were negatively correlated with the clinical variables related to T2DM in the BxD mouse genetic reference population database.ConclusionsUsing GA in miRNA-Seq data, we identified the optimal miRNA biomarker set for T2DM detection. GA can be a useful tool for biomarker discovery and drug-target identification.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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