ArticleFrontiers in endocrinology2023
Text mining-based identification of promising miRNA biomarkers for diabetes mellitus.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Clinical Importance of miRNA in Diabetic Neuropathy: Pathophysiology, Diagnosis, and Therapeutic Potential.Current diabetes reviews · 2026Review
- Upregulation of the Antioxidant Response-Related microRNAs miR-146a-5p and miR-21-5p in Gestational Diabetes: An Analysis of Matched Samples of Extracellular Vesicles and PBMCs.International journal of molecular sciences · 2025Article
- Text Mining Strategy Identifies Gene Networks Under Control of miR-21 in Breast Cancer Development.Cancer medicine · 2025Article
- The Role of Inflammation in the Pathogenesis of Diabetic Peripheral Neuropathy: New Lessons from Experimental Studies and Clinical Implications.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Review
- Cardiovascular risk factors and modern therapeutic strategies in children and adolescents with type 1 diabetes to prevent future diabetic angiopathy in the era of innovative miRNAs biomarkers.Frontiers in endocrinology · 2025Review
- Article
- Unique miRomics Expression Profiles inInternational journal of molecular sciences · 2023Article
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: MicroRNAs (miRNAs) are small, non-coding RNAs that play a critical role in diabetes development. While individual studies investigating the mechanisms of miRNA in diabetes provide valuable insights, their narrow focus limits their ability to provide a comprehensive understanding of miRNAs' role in diabetes pathogenesis and complications. Methods: To reduce potential bias from individual studies, we employed a text mining-based approach to identify the role of miRNAs in diabetes and their potential as biomarker candidates. Abstracts of publications were tokenized, and biomedical terms were extracted for topic modeling. Four machine learning algorithms, including Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines (SVM), were employed for diabetes classification. Feature importance was assessed to construct miRNA-diabetes networks. Results: Our analysis identified 13 distinct topics of miRNA studies in the context of diabetes, and miRNAs exhibited a topic-specific pattern. SVM achieved a promising prediction for diabetes with an accuracy score greater than 60%. Notably, miR-146 emerged as one of the critical biomarkers for diabetes prediction, targeting multiple genes and signal pathways implicated in diabetic inflammation and neuropathy. Conclusion: This comprehensive approach yields generalizable insights into the network miRNAs-diabetes network and supports miRNAs' potential as a biomarker for diabetes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.