Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.
Background. Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes. Methods. Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analys...
| Publicado en: | BioMed Research International pp. 1 - 16 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/4/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155581429&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155581429 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/4/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155581429 155581429 155581429 10.1155/2022/1230761 155581429 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm. aug: au: Li, Jiabin Ding, Jieying Zhi, D. U. Gu, Kaiyun Wang, Hui affil: Department of Pharmacy, Zhejiang University School of Medicine Children's Hospital, Hangzhou, China, 310052 sug: subj: Diabetes Mellitus, Type 2 Diagnosis Gene Expression Profiling Biological Markers Prediction Models Methods Support Vector Machine Algorithms Human Software Proteins Analysis Sensitivity and Specificity Pharmacokinetics ab: Background. Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes. Methods. Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes. Results. GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as "real" hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset. Conclusion. Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|