{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(3)"],"submitter":["Saxena A"],"pubmed_abstract":["Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve "],"journal":["Biomolecules"],"pagination":["432"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10046262"],"repository":["biostudies-literature"],"pubmed_title":["Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes."],"pmcid":["PMC10046262"],"pubmed_authors":["Mathur SK","Tiwari P","Saxena A","Mathur N","Pathak P"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes.","description":"Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-04-08T14:14:18.486Z","creation":"2025-02-19T02:38:10.967Z"},"accession":"S-EPMC10046262","cross_references":{"pubmed":["36979367"],"doi":["10.3390/biom13030432"]}}