Annual Review of Biomedical Data Science最新文献_第7页

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

Annual Review of Biomedical Data Science Pub Date : 2022-02-06 DOI: 10.1101/2022.01.31.22270183

S. Golder, K. O’Connor, Yunwen Wang, R. Stevens, G. Gonzalez-Hernandez

{"title":"Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases","authors":"S. Golder, K. O’Connor, Yunwen Wang, R. Stevens, G. Gonzalez-Hernandez","doi":"10.1101/2022.01.31.22270183","DOIUrl":"https://doi.org/10.1101/2022.01.31.22270183","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44284431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-05-11 DOI: 10.1146/annurev-biodatasci-092820-031008

Angela Oliveira Pisco, Bruno Tojo, Aaron McGeever

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

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-04-23 DOI: 10.1146/annurev-biodatasci-020121-084709

Tianming Zhou, Ruochi Zhang, Jian Ma

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

Annual Review of Biomedical Data Science 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

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

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-05-26 DOI: 10.1146/annurev-biodatasci-030421-030931

Bethany Percha

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

Annual Review of Biomedical Data Science 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

{"title":"African Global Representation in Biomedical Sciences.","authors":"Nicola Mulder, Lyndon Zass, Yosr Hamdi, Houcemeddine Othman, Sumir Panji, Imane Allali, Yasmina Jaufeerally Fakim","doi":"10.1146/annurev-biodatasci-102920-112550","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102920-112550","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39373761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Annual Review of Biomedical Data Science 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

{"title":"Satellite Monitoring for Air Quality and Health.","authors":"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","doi":"10.1146/annurev-biodatasci-110920-093120","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-110920-093120","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39373763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Neoantigen Controversies. 新抗原争议。

IF 7

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-05-11 DOI: 10.1146/annurev-biodatasci-092820-112713

Andrea Castro, Maurizio Zanetti, Hannah Carter

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

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 DOI: 10.1146/annurev-biodatasci-030221-125715

Sandra Soo-Jin Lee

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

Annual Review of Biomedical Data Science 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

{"title":"Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.","authors":"Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo, Chih-Hsuan Wei, Shankai Yan, Zhiyong Lu","doi":"10.1146/annurev-biodatasci-021821-061045","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-021821-061045","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39371087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27