ArticleBioMed research international2022
Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Integrative analysis of DEGs and regulatory networks in T2DM: identification of core genes and regulatory elements as novel therapeutic targets.3 Biotech · 2025Article
- Gene expression knowledge graph for patient representation and diabetes prediction.Journal of biomedical semantics · 2025Article
- Article
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Design of application-oriented disease diagnosis model using a meta-heuristic algorithm.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?Experimental and therapeutic medicine · 2023Article
- Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach.Biology · 2023Article
- Intracellular complement: Evidence, definitions, controversies, and solutions.Immunological reviews · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 ( Conclusion: Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.
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