生物医学数据科学家的人类微生物组和疾病。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI:10.1146/annurev-biodatasci-020722-043017
Jonathan L Golob
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引用次数: 0

摘要

人体微生物群是复杂的,因人而异,对健康至关重要,与疾病风险和治疗效果有关。有强大的技术可以用高通量测序来描述微生物群,并且在公共档案中有数十万个已经测序的标本。利用微生物组作为预测因素和精准医疗的目标仍然是有希望的。然而,当用作生物医学数据科学建模的输入时,微生物组呈现出独特的挑战。在这里,我们回顾了用于描述微生物群落的最常用技术,探索了这些独特的挑战,并讨论了生物医学数据科学家寻求将微生物组作为研究输入的更成功的方法。
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Human Microbiomes and Disease for the Biomedical Data Scientist.

The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.

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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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