机器学习在阿尔茨海默病研究中的应用:组学、成像和临床数据。

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Emerging Topics in Life Sciences Pub Date : 2021-12-21 DOI:10.1042/ETLS20210249
Ziyi Li, Xiaoqian Jiang, Yizhuo Wang, Yejin Kim
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引用次数: 15

摘要

阿尔茨海默病(AD)仍然是一种破坏性的神经退行性疾病,几乎没有可用的预防或治疗方法。高通量组学平台和成像设备的现代技术发展为研究这种疾病的病因和进展提供了前所未有的机会。与此同时,来自遗传学、蛋白质组学、转录组学和成像等各种模式的大量数据以及临床特征给数据整合和分析带来了巨大挑战。机器学习(ML)方法提供了新的技术来处理高维数据,整合来自不同来源的数据,对病因和临床异质性进行建模,并发现新的生物标志物。这些方向有可能帮助我们更好地控制疾病进展并制定新的治疗策略。这篇小型综述文章总结了使用单平台或多模态数据研究AD的不同ML方法。我们回顾了ML在AD研究的五个关键方向的应用现状:疾病分类、药物再利用、分型、进展预测和生物标志物发现。本综述深入了解了基于ML的AD研究的现状,并强调了未来研究的潜在方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

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7.70
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发文量
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