Infectious Disease Research in the Era of Big Data

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
P. Kasson
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引用次数: 6

Abstract

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.
大数据时代的传染病研究
传染病研究的范围从分子到全球——从病原体耐药性、毒力和复制的特定机制到世界各地的人、动物和病原体的运动。所有这些研究领域都受到了最近大规模数据源和数据分析的影响。其中一些进步依赖于大多数生物医学数据科学常见的数据或分析方法,而另一些则利用了传染病的独特性,即其传染性。本综述概述了过去几年的主要研究进展,并强调了一些剩余的机会,重点是数据或方法方法,特别是传染病。
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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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