在阿尔茨海默病研究中采用信息学策略:从遗传学、多组学、生物标记物到临床结果的回顾。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jingxuan Bao, Brian N Lee, Junhao Wen, Mansu Kim, Shizhuo Mu, Shu Yang, Christos Davatzikos, Qi Long, Marylyn D Ritchie, Li Shen
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引用次数: 0

摘要

阿尔茨海默氏症(AD)是一个全国性的重大问题,影响到 580 万人,每年造成的损失超过 2,500 亿美元。然而,目前尚无治疗方法。因此,迫切需要有效的策略来发现阿兹海默症生物标志物,以用于疾病的早期检测和药物开发。在这篇综述中,我们从生物医学数据科学家的角度研究了AD,讨论了AD研究的四个基本组成部分:遗传学(G)、分子多组学(M)、多模态成像生物标志物(B)和临床结果(O)(统称为GMBO框架)。我们全面回顾了 GMBO 框架中每个组成部分的常用统计和信息学方法,并附有具有里程碑意义的 AD 研究的主要发现。我们的综述强调了多模态生物库数据在应对 AD 关键挑战(如早期诊断、疾病异质性和治疗开发)方面的潜力。我们指出了 AD 研究中的主要障碍,包括数据稀缺性和复杂性,并倡导加强合作、统一数据和采用先进的建模技术。这篇综述旨在成为了解当前 AD 研究中生物医学数据科学策略的重要指南,强调我们需要综合、多学科的方法来促进我们对 AD 的理解和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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