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The digital revolution in phenotyping

View Article: PubMed Central - PubMed

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.

No MeSH data available.


The four dimensions of the phenotype development phases. Representation: Subject to the underlying domain and goal, phenotypes may be represented at different granularity levels. Interoperability: Existing ontologies and vocabularies externalize domain-specific phenotype knowledge at different levels of granularity. Acquisition: Capturing and documenting phenotypes in any representational format can be achieved manually (via curation) or automatically (via text mining). Processing: Representing and capturing phenotypes in a structured manner (a form that also enables interoperability) has led to their application in a large variety of domains. The arrows denote direct points of connection between the several phenotyping dimensions. Note that this figure only serves as illustration of the interplay of the four dimension and, thus, is not aimed at comprehensiveness (e.g. interoperability could also be achieved with a mapping instead of EQ statements).
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bbv083-F1: The four dimensions of the phenotype development phases. Representation: Subject to the underlying domain and goal, phenotypes may be represented at different granularity levels. Interoperability: Existing ontologies and vocabularies externalize domain-specific phenotype knowledge at different levels of granularity. Acquisition: Capturing and documenting phenotypes in any representational format can be achieved manually (via curation) or automatically (via text mining). Processing: Representing and capturing phenotypes in a structured manner (a form that also enables interoperability) has led to their application in a large variety of domains. The arrows denote direct points of connection between the several phenotyping dimensions. Note that this figure only serves as illustration of the interplay of the four dimension and, thus, is not aimed at comprehensiveness (e.g. interoperability could also be achieved with a mapping instead of EQ statements).

Mentions: To enable a structured navigation of the field, we map the content of the review onto the four conceptual dimensions of phenomics, considered from a computational perspective (depicted in Figure 1 ): representation, interoperability, acquisition and processing. These four dimensions have been identified and described in an earlier review [21] and are used for simplicity here. Representation focuses on semantic modeling and aspects of knowledge capturing. Interoperability, an orthogonal dimension to representation, aims to facilitate intra- and interspecies phenotype mappings. Acquisition refers to the transformation of the raw data into semi-structured or structured phenotype representations. Processing (or application) uses externalized phenotypes to address fundamental or specific challenges, from variant prioritization and diagnosis to individualized preventive care or drug repurposing.Figure 1.


The digital revolution in phenotyping
The four dimensions of the phenotype development phases. Representation: Subject to the underlying domain and goal, phenotypes may be represented at different granularity levels. Interoperability: Existing ontologies and vocabularies externalize domain-specific phenotype knowledge at different levels of granularity. Acquisition: Capturing and documenting phenotypes in any representational format can be achieved manually (via curation) or automatically (via text mining). Processing: Representing and capturing phenotypes in a structured manner (a form that also enables interoperability) has led to their application in a large variety of domains. The arrows denote direct points of connection between the several phenotyping dimensions. Note that this figure only serves as illustration of the interplay of the four dimension and, thus, is not aimed at comprehensiveness (e.g. interoperability could also be achieved with a mapping instead of EQ statements).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036847&req=5

bbv083-F1: The four dimensions of the phenotype development phases. Representation: Subject to the underlying domain and goal, phenotypes may be represented at different granularity levels. Interoperability: Existing ontologies and vocabularies externalize domain-specific phenotype knowledge at different levels of granularity. Acquisition: Capturing and documenting phenotypes in any representational format can be achieved manually (via curation) or automatically (via text mining). Processing: Representing and capturing phenotypes in a structured manner (a form that also enables interoperability) has led to their application in a large variety of domains. The arrows denote direct points of connection between the several phenotyping dimensions. Note that this figure only serves as illustration of the interplay of the four dimension and, thus, is not aimed at comprehensiveness (e.g. interoperability could also be achieved with a mapping instead of EQ statements).
Mentions: To enable a structured navigation of the field, we map the content of the review onto the four conceptual dimensions of phenomics, considered from a computational perspective (depicted in Figure 1 ): representation, interoperability, acquisition and processing. These four dimensions have been identified and described in an earlier review [21] and are used for simplicity here. Representation focuses on semantic modeling and aspects of knowledge capturing. Interoperability, an orthogonal dimension to representation, aims to facilitate intra- and interspecies phenotype mappings. Acquisition refers to the transformation of the raw data into semi-structured or structured phenotype representations. Processing (or application) uses externalized phenotypes to address fundamental or specific challenges, from variant prioritization and diagnosis to individualized preventive care or drug repurposing.Figure 1.

View Article: PubMed Central - PubMed

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.

No MeSH data available.