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Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases 大数据分析的最佳实践,以解决我们对疾病病因、诊断和预后的理解中的性别特异性偏差
IF 6 Pub Date : 2022-02-06 DOI: 10.1101/2022.01.31.22270183
S. Golder, K. O’Connor, Yunwen Wang, R. Stevens, G. Gonzalez-Hernandez
A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.
健康研究中倾向于理解男性疾病的偏见可能会对女性健康产生严重影响。本文报告了对使用机器学习或NLP技术询问大数据以识别性别特定健康差异的文献的概念性综述。2021年10月,我们使用同义词和索引词搜索了Ovid MEDLINE、Embase和PsycINFO,分别为(1)“女性”或“男性”或“性别”,(2)“大数据”或“人工智能”或“NLP”,以及(3)“差异”或“差异”。从902份记录中,有22项研究符合纳入标准并进行了分析。结果表明,按性别划分的纳入情况是不一致的,而且往往没有报告,尽管纳入研究的男性比例远远低于女性。尽管人工智能和NLP技术在健康研究中得到了广泛应用,但很少有研究使用它们来支持非结构化文本来调查与性别相关的差异或差异。研究人员越来越意识到基于性别的数据偏见,但纠正过程很慢。我们思考了使用大数据分析来解决在理解疾病病因、诊断和预后方面存在的性别偏见的最佳做法。
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引用次数: 1
Single-Cell Analysis for Whole-Organism Datasets. 全生物数据集的单细胞分析。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-05-11 DOI: 10.1146/annurev-biodatasci-092820-031008
Angela Oliveira Pisco, Bruno Tojo, Aaron McGeever

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

细胞图谱是基因组的重要伙伴,因为它们阐明了基因如何以特定细胞类型的方式使用,或者基因的使用如何在生物体的一生中发生变化。这篇综述探讨了生物体单细胞图谱的最新进展,使我们能够理解健康和疾病中的细胞异质性、组织和细胞命运。在这里,我们概述了最近建立跨物种细胞图谱的努力,并讨论了该领域目前面临的挑战。此外,我们提出了一个概念,即拥有一个可以随着实验和计算方法的数量而扩展的知识库,以及一个新的反馈回路,用于包括用户贡献的计算方法的开发和基准测试。这两个方面是单细胞生物学社区努力的关键,这将有助于以无与伦比的分辨率产生细胞类型和状态的综合注释图。
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引用次数: 4
The 3D Genome Structure of Single Cells. 单细胞的三维基因组结构。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-04-23 DOI: 10.1146/annurev-biodatasci-020121-084709
Tianming Zhou, Ruochi Zhang, Jian Ma

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts.

基因组在细胞核中的空间组织对细胞功能至关重要。然而,三维基因组组织及其动力学如何影响细胞表型仍然知之甚少。用于探测3D基因组的单细胞技术的最新发展,特别是单细胞Hi-C (scHi-C),以前所未有的分辨率开启了揭示3D基因组特征的细胞间变异性的新时代。在这里,我们回顾了scHi-C分析的计算方法的最新进展,包括数据处理、降维、提高数据质量的imputation以及单细胞分辨率下3D基因组特征的揭示。虽然在分析单细胞三维基因组的计算方法开发方面取得了很大进展,但需要大量的未来工作来改进数据解释和多模态数据集成,这对于揭示不同生物学背景下异质细胞群体中基因组结构和功能之间的基本联系至关重要。
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引用次数: 29
Integration of Multimodal Data for Deciphering Brain Disorders. 多模态数据集成用于脑部疾病的破译。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-04-23 DOI: 10.1146/annurev-biodatasci-092820-020354
Jingqi Chen, Guiying Dong, Liting Song, Xingzhong Zhao, Jixin Cao, Xiaohui Luo, Jianfeng Feng, Xing-Ming Zhao

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

人类大脑在正常和疾病条件下的大量多模态数据的积累,为理解大脑疾病产生的原因和方式提供了前所未有的机会。与传统的单一数据集分析相比,涵盖不同类型数据(即基因组学、转录组学、成像等)的多模态数据集的整合,在微观和宏观层面上更详细地揭示了大脑疾病的机制。在这篇综述中,我们首先简要介绍了流行的大脑大数据集。然后,我们详细讨论了多模态人脑数据集的整合如何揭示大脑疾病的遗传易感性和异常分子途径。最后,我们展望了未来数据整合工作将如何促进脑部疾病的诊断和治疗。
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引用次数: 3
Modern Clinical Text Mining: A Guide and Review. 现代临床文本挖掘:指南与综述。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-05-26 DOI: 10.1146/annurev-biodatasci-030421-030931
Bethany Percha

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry.

电子健康记录(EHRs)正在成为医疗保健质量改进、研究和运营的重要数据来源。然而,电子病历中包含的许多最有价值的信息仍然隐藏在非结构化的文本中。近年来,临床文本挖掘领域发展迅速,从基于规则的方法过渡到机器学习,以及最近的深度学习。然而,新方法带来了新的挑战,特别是对那些刚进入该领域的人来说。这篇综述为那些第一次遇到临床文本挖掘的人(例如,医生研究人员,操作分析团队,来自其他领域的机器学习科学家)提供了临床文本挖掘的概述。虽然不是一个全面的调查,但这篇综述描述了最新的技术状况,特别关注了过去几年开发的新任务和方法。它还确定了这些显著的技术进步与卫生系统和工业实施的实际现实之间的主要障碍。
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引用次数: 0
African Global Representation in Biomedical Sciences. 非洲在生物医学科学领域的全球代表性。
IF 6 Pub Date : 2021-07-20 DOI: 10.1146/annurev-biodatasci-102920-112550
Nicola Mulder, Lyndon Zass, Yosr Hamdi, Houcemeddine Othman, Sumir Panji, Imane Allali, Yasmina Jaufeerally Fakim

African populations are diverse in their ethnicity, language, culture, and genetics. Although plagued by high disease burdens, until recently the continent has largely been excluded from biomedical studies. Along with limitations in research and clinical infrastructure, human capacity, and funding, this omission has resulted in an underrepresentation of African data and disadvantaged African scientists. This review interrogates the relative abundance of biomedical data from Africa, primarily in genomics and other omics. The visibility of African science through publications is also discussed. A challenge encountered in this review is the relative lack of annotation of data on their geographical or population origin, with African countries represented as a single group. In addition to the abovementioned limitations,the global representation of African data may also be attributed to the hesitation to deposit data in public repositories. Whatever the reason, the disparity should be addressed, as African data have enormous value for scientists in Africa and globally.

非洲人口在种族、语言、文化和基因上都是多样化的。尽管疾病负担沉重,但直到最近,非洲大陆在很大程度上一直被排除在生物医学研究之外。加上研究和临床基础设施、人员能力和资金方面的限制,这种遗漏导致了非洲数据的代表性不足,并使非洲科学家处于不利地位。这篇综述询问了来自非洲的相对丰富的生物医学数据,主要是基因组学和其他组学。还讨论了通过出版物提高非洲科学的知名度。本审查遇到的一个挑战是相对缺乏对其地理或人口来源的数据的注释,非洲国家作为一个单一的群体。除了上述限制之外,非洲数据的全球代表性也可能归因于对将数据存入公共存储库的犹豫。不管是什么原因,这种差异应该得到解决,因为非洲的数据对非洲和全球的科学家都有巨大的价值。
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引用次数: 4
Satellite Monitoring for Air Quality and Health. 空气质量和健康卫星监测。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-06-01 DOI: 10.1146/annurev-biodatasci-110920-093120
Tracey Holloway, Daegan Miller, Susan Anenberg, Minghui Diao, Bryan Duncan, Arlene M Fiore, Daven K Henze, Jeremy Hess, Patrick L Kinney, Yang Liu, Jessica L Neu, Susan M O'Neill, M Talat Odman, R Bradley Pierce, Armistead G Russell, Daniel Tong, J Jason West, Mark A Zondlo

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM2.5) and nitrogen dioxide (NO2). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

来自卫星仪器的数据提供了对与人类健康有关的气体和颗粒水平的估计,甚至是人眼看不见的污染物。然而,要成功地解释卫星数据,就需要了解卫星与其他数据源的关系,以及影响其应用于卫生挑战的因素。根据2016-2020年NASA健康和空气质量应用科学小组的专业知识和经验,我们对空气质量和健康应用的卫星数据进行了审查。我们讨论了用于流行病学研究和健康影响评估的卫星数据,以及利用卫星数据评估空气质量趋势、支持空气质量监管、描述野火烟雾特征和量化排放源。与空气质量监测站等现场测量数据相比,卫星数据的主要优势在于其空间覆盖范围。卫星数据可以揭示世界上污染水平最高的地方,污染水平在每天到十年的时间内是如何变化的,以及污染物从城市到全球范围内的运输位置。迄今为止,空气质量和健康应用主要利用卫星观测和与近地表颗粒物(2.5)和二氧化氮(NO2)相关的卫星衍生产品。卫生和空气质量领域越来越多地使用卫星数据,预计这一趋势将继续下去。从卫生研究人员到空气质量管理人员,从全球应用到社区影响,卫星数据正在改变评估空气污染暴露的方式。
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引用次数: 18
Neoantigen Controversies. 新抗原争议。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2021-07-20 Epub Date: 2021-05-11 DOI: 10.1146/annurev-biodatasci-092820-112713
Andrea Castro, Maurizio Zanetti, Hannah Carter

Next-generation sequencing technologies have revolutionized our ability to catalog the landscape of somatic mutations in tumor genomes. These mutations can sometimes create so-called neoantigens, which allow the immune system to detect and eliminate tumor cells. However, efforts that stimulate the immune system to eliminate tumors based on their molecular differences have had less success than has been hoped for, and there are conflicting reports about the role of neoantigens in the success of this approach. Here we review some of the conflicting evidence in the literature and highlight key aspects of the tumor-immune interface that are emerging as major determinants of whether mutation-derived neoantigens will contribute to an immunotherapy response. Accounting for these factors is expected to improve success rates of future immunotherapy approaches.

下一代测序技术彻底改变了我们对肿瘤基因组中体细胞突变的编目能力。这些突变有时会产生所谓的新抗原,使免疫系统能够检测并消灭肿瘤细胞。然而,根据肿瘤的分子差异来刺激免疫系统消灭肿瘤的努力并没有取得预期的成功,关于新抗原在这种方法的成功中所起的作用,也有相互矛盾的报道。在此,我们回顾了文献中一些相互矛盾的证据,并强调了肿瘤免疫界面的一些关键方面,这些方面正在成为突变衍生的新抗原是否会促进免疫疗法反应的主要决定因素。考虑到这些因素有望提高未来免疫疗法的成功率。
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引用次数: 0
The Ethics of Consent in a Shifting Genomic Ecosystem. 在不断变化的基因组生态系统中的同意伦理。
IF 6 Pub Date : 2021-07-20 DOI: 10.1146/annurev-biodatasci-030221-125715
Sandra Soo-Jin Lee

The collection and use of human genetic data raise important ethical questions about how to balance individual autonomy and privacy with the potential for public good. The proliferation of local, national, and international efforts to collect genetic data and create linkages to support large-scale initiatives in precision medicine and the learning health system creates new demands for broad data sharing that involve managing competing interests and careful consideration of what constitutes appropriate ethical trade-offs. This review describes these emerging ethical issues with a focus on approaches to consent and issues related to justice in the shifting genomic research ecosystem.

人类基因数据的收集和使用引发了重要的伦理问题,即如何在个人自主权和隐私与潜在的公共利益之间取得平衡。地方、国家和国际上收集遗传数据和建立联系以支持精准医学和学习型卫生系统的大规模倡议的努力的扩散,对广泛的数据共享产生了新的需求,这涉及管理相互竞争的利益和仔细考虑什么构成适当的伦理权衡。这篇综述描述了这些新兴的伦理问题,重点是在不断变化的基因组研究生态系统中,同意的方法和与正义相关的问题。
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引用次数: 6
Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing. 人工智能在行动:用自然语言处理应对COVID-19大流行。
IF 6 Pub Date : 2021-07-20 Epub Date: 2021-05-14 DOI: 10.1146/annurev-biodatasci-021821-061045
Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo, Chih-Hsuan Wei, Shankai Yan, Zhiyong Lu

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.

COVID-19(2019冠状病毒病)大流行对社会产生了重大影响,这既是因为COVID-19对健康造成严重影响,也是因为为减缓其传播而采取的公共卫生措施。其中许多困难从根本上说是信息需求;试图解决这些需求已经造成了研究人员和公众的信息过载。自然语言处理(NLP)是解释人类语言的人工智能分支,可用于解决因COVID-19大流行而迫切需要的许多信息需求。本综述调查了关于COVID-19大流行的约150项NLP研究和50多个系统和数据集。我们详细介绍了四个核心NLP任务:信息检索、命名实体识别、基于文献的发现和问题回答。我们还描述了通过四项额外任务直接解决大流行方面的工作:主题建模、情绪和情绪分析、病例量预测和错误信息检测。最后,我们讨论了可观察到的趋势和仍然存在的挑战。
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引用次数: 27
期刊
Annual Review of Biomedical Data Science
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