Pathway to precision: machine learning and bioinformatics in diabetes gene expression studies

Diabetes mellitus is a metabolic disorder; understanding the pathogenic mechanisms underlying diabetes is crucial. Analyzing biomarkers, supported by machine learning and bioinformatics, is crucial for identifying the molecular causes of diabetes.

This study summarizes the current advances in diabetes research, highlighting significant progress in bioinformatics, gene expression analysis, and machine learning.

The search was conducted in Google Scholar, PubMed, and Scopus using two sets of keywords, including terms like diabetes, biomarkers, bioinformatics, and machine learning. The selection process followed PRISMA guidelines. The inclusion criteria targeted open-access English articles published from 2020 to 2024, resulting in a final selection of 96 articles.

The findings were grouped into six categories: data acquisition methods, complications, bioinformatics techniques, machine learning methods, promotion, and evaluation. Microarrays were the most frequent technique for data collection, with 70 occurrences. The most common bioinformatics method was KEGG pathway analysis (84 instances). The most frequently used machine learning techniques were LASSO (43) and PCA (47). Prognostic applications were reported 45 times, while diagnostic applications appeared 51 times.

Recent studies have highlighted the importance of KEGG pathway analysis and microarray datasets in diabetes research. Newer technologies, such as RNA-seq and single-cell RNA-seq, remain underutilized despite significant advancements in their development. A greater focus on novel biological pathways, the development of biomarkers, and a more comprehensive application of machine learning techniques could all aid in personalized treatment for diabetes. Future research should aim to overcome technological limitations and integrate clinical data with bioinformatics workflows to enhance translational relevance and promote precision medicine.