基于机器学习的口腔癌诊断中的 miRNA。

IF 3.9 3区 工程技术 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Xinghang Li, Valentina L Kouznetsova, Igor F Tsigelny
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引用次数: 0

摘要

背景:微RNA(miRNA)是基因表达的关键调控因子,在包括癌症发病机制在内的各种细胞过程中发挥着重要作用。传统的癌症诊断方法,如活检和组织病理学分析,虽然有效,但具有侵入性、成本高,而且需要专业技能。随着全球癌症发病率的不断上升,迫切需要更方便、创伤更小的诊断替代方法:本研究探讨了基于 miRNA 属性的机器学习 (ML) 模型作为口腔癌非侵入性诊断工具的潜力。方法和工具:我们采用了一个全面的方法框架,涉及 miRNA 属性的生成,包括序列特征、靶基因关联和癌症特异性信号通路:我们使用各种多线性算法对miRNA进行了分类,其中BayesNet分类器表现出色,准确率达到95%,交叉验证时接收者工作特征曲线下面积(AUC)为0.98。该模型的有效性在独立数据集上得到了进一步验证,证实了其潜在的临床实用性:讨论:我们的研究结果凸显了基于 miRNA 的 ML 模型在加强早期癌症检测、减轻医疗负担和挽救生命方面的前景:这项研究为未来的 miRNA 生物标记物研究铺平了道路,为各种癌症提供了一种可扩展、可调整的诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
miRNA in Machine-Learning-Based Diagnostics of Oral Cancer.

Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives.

Objective: This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways.

Results: The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model's effectiveness was further validated using independent datasets, confirming its potential clinical utility.

Discussion: Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives.

Conclusions: This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.

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来源期刊
Biomedicines
Biomedicines Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.20
自引率
8.50%
发文量
2823
审稿时长
8 weeks
期刊介绍: Biomedicines (ISSN 2227-9059; CODEN: BIOMID) is an international, scientific, open access journal on biomedicines published quarterly online by MDPI.
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