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The digital revolution in phenotyping

Oellrich, Anika
; 
Collier, Nigel
; 
Groza, Tudor
; 
Rebholz-Schuhmann, Dietrich
; 
Shah, Nigam
; 
Bodenreider, Olivier
; 
Boland, Mary Regina
; 
Georgiev, Ivo
; 
Liu, Hongfang
; 
Livingston, Kevin
... show 9 more
Repository DOI
Publication Date
2015-09-29
Keywords
phenomics, phenotypes, acquisition, interoperability, semantic representation, knowledge discovery, model organism database, genome-wide association, pharmacogenetics knowledge, clinical text, human phenome, gene-function, ontologies, resource, disease, information
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Article
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Citation
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 (2015). The digital revolution in phenotyping. Briefings in Bioinformatics 17 (5), 819-830
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.
Funder
Publisher
Oxford University Press (OUP)
Publisher DOI
10.1093/bib/bbv083
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Externally hosted open access publications with University of Galway authors (2)