遗传和临床患者数据的大规模分析

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
M. Ritchie
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引用次数: 13

摘要

生物医学数据科学在过去十年中经历了新数据的爆炸式增长。由于现代分子技术的成熟,大量的遗传和基因组数据越来越多地出现在大型、多样化的数据集中。除了这些分子数据外,还可以从卫生保健提供者组织、临床试验、人口健康登记和流行病学研究的综合临床数据集中获得密集、丰富的表型数据。随着我们对这些问题和挑战的理解不断出现,研究这些大型遗传/基因组和临床数据集的方法和方法也在迅速发展。在这篇综述中,最新的遗传/基因组分析方法以及复杂的表型组学将被讨论。这个领域正在改变和适应新的数据类型,以及计算和机器学习方面的技术进步。因此,我也将讨论这个令人兴奋和创新的领域未来的挑战。精准医疗的前景在很大程度上依赖于以有意义的方式将复杂的遗传/基因组数据与临床表型结合起来的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Analysis of Genetic and Clinical Patient Data
Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.
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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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