Central Hospital Affiliated to Shandong First Medical University, Ophthalmology Department, Jinan, Shandong, China.
Oakland University William Beaumont School of Medicine, Rochester, MI, United States.
Front Endocrinol (Lausanne). 2023 Jul 25;14:1195145. doi: 10.3389/fendo.2023.1195145. eCollection 2023.
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.
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.
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.
This comprehensive approach yields generalizable insights into the network miRNAs-diabetes network and supports miRNAs' potential as a biomarker for diabetes.
MicroRNAs（miRNAs）是一种小型的非编码 RNA，在糖尿病的发展中起着至关重要的作用。虽然个别研究调查了 miRNA 在糖尿病中的作用机制，提供了有价值的见解，但它们的狭隘焦点限制了它们提供对 miRNA 在糖尿病发病机制和并发症中的作用的全面理解的能力。
为了减少来自个别研究的潜在偏差，我们采用基于文本挖掘的方法来确定 miRNA 在糖尿病中的作用及其作为生物标志物候选物的潜力。将出版物的摘要进行分词，并提取生物医学术语进行主题建模。我们使用了四种机器学习算法，包括朴素贝叶斯、决策树、随机森林和支持向量机（SVM），来进行糖尿病分类。评估特征重要性以构建 miRNA-糖尿病网络。
我们的分析确定了 13 个不同的 miRNA 研究主题，这些主题与糖尿病有关，并且 miRNA 表现出特定主题的模式。SVM 对糖尿病的预测具有令人鼓舞的准确率，准确率评分超过 60%。值得注意的是，miR-146 作为糖尿病预测的关键生物标志物之一，针对多个基因和信号通路，这些基因和信号通路与糖尿病炎症和神经病变有关。
这种全面的方法产生了关于 miRNA-糖尿病网络的可推广的见解，并支持 miRNA 作为糖尿病生物标志物的潜力。
