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The digital revolution in phenotyping
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 on...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036847/ https://www.ncbi.nlm.nih.gov/pubmed/26420780 http://dx.doi.org/10.1093/bib/bbv083 |
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author | 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 |
author_facet | 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 |
author_sort | Oellrich, Anika |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5036847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-50368472016-09-27 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 Luna, Augustin Mallon, Ann-Marie Manda, Prashanti Robinson, Peter N. Rustici, Gabriella Simon, Michelle Wang, Liqin Winnenburg, Rainer Dumontier, Michel Brief Bioinform Papers 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. Oxford University Press 2016-09 2015-09-29 /pmc/articles/PMC5036847/ /pubmed/26420780 http://dx.doi.org/10.1093/bib/bbv083 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Papers 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 The digital revolution in phenotyping |
title | The digital revolution in phenotyping |
title_full | The digital revolution in phenotyping |
title_fullStr | The digital revolution in phenotyping |
title_full_unstemmed | The digital revolution in phenotyping |
title_short | The digital revolution in phenotyping |
title_sort | digital revolution in phenotyping |
topic | Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036847/ https://www.ncbi.nlm.nih.gov/pubmed/26420780 http://dx.doi.org/10.1093/bib/bbv083 |
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