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表型分析中的数字革命。

The digital revolution in phenotyping.

作者信息

Oellrich Anika, Collier Nigel, Groza Tudor, Rebholz-Schuhmann Dietrich, Shah Nigam, Bodenreider Olivier, Boland Mary Regina, Georgiev Ivo, Liu Hongfang, Livingston Kevin, Luna Augustin, Mallon Ann-Marie, Manda Prashanti, Robinson Peter N, Rustici Gabriella, Simon Michelle, Wang Liqin, Winnenburg Rainer, Dumontier Michel

出版信息

Brief Bioinform. 2016 Sep;17(5):819-30. doi: 10.1093/bib/bbv083. Epub 2015 Sep 29.

Abstract

Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support 'bench to bedside' efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

摘要

由于表型在疾病基因和药物靶点发现、系统发育学和药物基因组学等众多领域的应用,它们在临床和生物学领域越来越受到关注。表型被定义为生物体的可观察特征,可以被视为将实验结果转化为临床应用的桥梁之一,从而支持“从 bench 到 bedside”的努力。然而,为了构建这座转化桥梁,需要对表型有一个超越特定领域定义的共同和普遍的理解。为了实现这一宏伟目标,一场数字革命正在进行,它能够将数据编码为计算机可读格式,并将数据存储在专门的存储库中,以便进行集成,从而推动转化研究。虽然表型组研究是一项持续的工作,但目前可用数据中隐藏的真正潜力仍有待挖掘,这为未来几年提供了令人兴奋的机会。在这里,我们通过表示、获取和分析表型数据,深入了解数字表型的最新进展。此外,我们还展望了该领域未来的研究工作,这些工作可能会使表型数据得到更好的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d21c/5036847/56a160ebb50b/bbv083f1p.jpg

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