期刊
BRIEFINGS IN BIOINFORMATICS
卷 17, 期 5, 页码 819-830出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbv083
关键词
phenomics; phenotypes; acquisition; interoperability; semantic representation; knowledge discovery
资金
- National Institutes of Health [1 U54 HG006370-01, R01 LM011369, R01 GM101430, U54 HG004028, T15 LM00707, R01-LM008111, R01 GM102282, U24 CA143840, U54HG006370, U54 HG008033-01]
- Wellcome Trust [098051]
- Marie Curie experience researcher fellowship [301806]
- National Science Foundation [1207592, DBI-1062404, DBI-1062542, EF-0905606]
- Bundesministerium fur Bildung und Forschung [0313911]
- European Community's Seventh Framework Programme [602300]
- European Community's Seventh Framework Programme [SYBIL]
- Systems Microscopy NoE project [258068]
- Defense Advanced Research Projects Agency [W911NF-14-C-0109]
- Kinghorn Foundation
- Intramural Research Program of the NIH, National Library of Medicine
- Medical Research Council
- Homer Warner Center for Informatics Research of the IHC Health Services
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1062542] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1207592] Funding Source: National Science Foundation
- Medical Research Council [MR/M025160/1] Funding Source: researchfish
- MRC [MR/M025160/1, MC_U142684171] Funding Source: UKRI
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.
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