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Text mining-based identification of promising miRNA biomarkers for diabetes mellitus
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 miR...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407569/ https://www.ncbi.nlm.nih.gov/pubmed/37560309 http://dx.doi.org/10.3389/fendo.2023.1195145 |
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author | Li, Xin Dai, Andrea Tran, Richard Wang, Jie |
author_facet | Li, Xin Dai, Andrea Tran, Richard Wang, Jie |
author_sort | Li, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10407569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104075692023-08-09 Text mining-based identification of promising miRNA biomarkers for diabetes mellitus Li, Xin Dai, Andrea Tran, Richard Wang, Jie Front Endocrinol (Lausanne) Endocrinology 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. Frontiers Media S.A. 2023-07-25 /pmc/articles/PMC10407569/ /pubmed/37560309 http://dx.doi.org/10.3389/fendo.2023.1195145 Text en Copyright © 2023 Li, Dai, Tran and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Li, Xin Dai, Andrea Tran, Richard Wang, Jie Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title | Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title_full | Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title_fullStr | Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title_full_unstemmed | Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title_short | Text mining-based identification of promising miRNA biomarkers for diabetes mellitus |
title_sort | text mining-based identification of promising mirna biomarkers for diabetes mellitus |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407569/ https://www.ncbi.nlm.nih.gov/pubmed/37560309 http://dx.doi.org/10.3389/fendo.2023.1195145 |
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