非洲在生物医学科学领域的全球代表性。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 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
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引用次数: 4

摘要

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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African Global Representation in Biomedical Sciences.

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.

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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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