基于 miRNA 生物标志物和深度学习的帕金森病诊断。
Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.
机构信息
REHS Program, San Diego Supercomputer Center, UC San Diego, La Jolla, CA 92093, USA.
San Diego Supercomputer Center, UC San Diego, La Jolla, CA 92093, USA.
出版信息
Front Biosci (Landmark Ed). 2024 Jan 12;29(1):4. doi: 10.31083/j.fbl2901004.
BACKGROUND
The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.
METHODS
We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.
RESULTS
The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis.
CONCLUSIONS
The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.
背景
目前帕金森病（PD）的诊断标准往往不够精确且费用高昂。然而，microRNA（miRNA）的失调模式有望成为一种可靠有效的非侵入性 PD 诊断方法。
方法
我们使用数据挖掘来阐明新的 miRNA 生物标志物，然后基于这些生物标志物开发一种机器学习（ML）模型来诊断 PD。
结果
在经过滤的 PD 失调 miRNA 上进行训练的表现最佳的 ML 模型，能够以 95.65%的准确率识别 miRNA 生物标志物。通过对与 PD 相关的 miRNA 进行分析，创建了数千个依赖于基因靶点的描述符，可用于识别新的生物标志物并加强 PD 诊断。
结论
基于 miRNA 及其基因组途径描述符开发的 ML 模型在 PD 的预测方面取得了很高的准确率。
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