The digital revolution in phenotyping

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Web of Science)

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.? The Author 2015. Published by Oxford University Press.
Original languageEnglish
Pages (from-to)819-830
JournalBriefings in Bioinformatics
Volume17
Issue number5
DOIs
Publication statusPublished - Sep 2016
Externally publishedYes

Keywords

  • phenomics
  • phenotypes
  • acquisition
  • interoperability
  • semantic representation
  • knowledge discovery

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