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• W4317509941 abstract "MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer’s disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs’ potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease’s comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods." @default.
• W4317509941 created "2023-01-20" @default.
• W4317509941 creator A5004449789 @default.
• W4317509941 creator A5016213975 @default.
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• W4317509941 creator A5072077826 @default.
• W4317509941 creator A5085529612 @default.
• W4317509941 creator A5088230370 @default.
• W4317509941 date "2023-01-19" @default.
• W4317509941 modified "2023-10-14" @default.
• W4317509941 title "Type 2 Diabetes Mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning" @default.
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• W4317509941 doi "https://doi.org/10.3389/fendo.2022.1084656" @default.
• W4317509941 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36743910" @default.
• W4317509941 hasPublicationYear "2023" @default.
• W4317509941 type Work @default.
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• W4317509941 hasAuthorship W4317509941A5004449789 @default.
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• W4317509941 hasAuthorship W4317509941A5047057719 @default.

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