Limits...
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.


To date, phenotypes have mostly been captured and defined using a pre-composed and/or a post-composed representation. A pre-composed representation assumes the definition of a phenotype as a monolithic concept—a concept that captures the essence of the phenotype semantics. The post-composed representation decomposes the phenotype into an Entity–Quality pair, with its individual components being mapped to appropriate ontological concepts. In this case, the phenotype semantics is denoted by the compositional property of the pair. The transition between pre-composed and post-composed is realized via logical axioms. Both forms of representation have been successfully applied across different species.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036847&req=5

bbv083-F2: To date, phenotypes have mostly been captured and defined using a pre-composed and/or a post-composed representation. A pre-composed representation assumes the definition of a phenotype as a monolithic concept—a concept that captures the essence of the phenotype semantics. The post-composed representation decomposes the phenotype into an Entity–Quality pair, with its individual components being mapped to appropriate ontological concepts. In this case, the phenotype semantics is denoted by the compositional property of the pair. The transition between pre-composed and post-composed is realized via logical axioms. Both forms of representation have been successfully applied across different species.

Mentions: Currently, the field consists of a varied set of vocabularies and ontologies that support, in various forms, the abovementioned goal. In particular, driven by the wide adoption from the biomedical community, ontologies have become the de facto standard for representing phenotypes. To achieve the goal to its full extent, the community has followed two complementary approaches for modeling and integrating phenotype data: a pre-composed and a post-composed representation (see Figure 2). The pre-composed approach treats each phenotype as an atomic entity, using individual expressions most suitable to general human understanding. For example, an ontology adopting this representation consists of concept definitions like ‘erythrocytopenia’ or ‘deficiency of red blood cells’ / ‘deficiency of erythrocytes’. These concepts are easily understood by humans and also facilitate computational analysis.


The digital revolution in phenotyping
To date, phenotypes have mostly been captured and defined using a pre-composed and/or a post-composed representation. A pre-composed representation assumes the definition of a phenotype as a monolithic concept—a concept that captures the essence of the phenotype semantics. The post-composed representation decomposes the phenotype into an Entity–Quality pair, with its individual components being mapped to appropriate ontological concepts. In this case, the phenotype semantics is denoted by the compositional property of the pair. The transition between pre-composed and post-composed is realized via logical axioms. Both forms of representation have been successfully applied across different species.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

bbv083-F2: To date, phenotypes have mostly been captured and defined using a pre-composed and/or a post-composed representation. A pre-composed representation assumes the definition of a phenotype as a monolithic concept—a concept that captures the essence of the phenotype semantics. The post-composed representation decomposes the phenotype into an Entity–Quality pair, with its individual components being mapped to appropriate ontological concepts. In this case, the phenotype semantics is denoted by the compositional property of the pair. The transition between pre-composed and post-composed is realized via logical axioms. Both forms of representation have been successfully applied across different species.
Mentions: Currently, the field consists of a varied set of vocabularies and ontologies that support, in various forms, the abovementioned goal. In particular, driven by the wide adoption from the biomedical community, ontologies have become the de facto standard for representing phenotypes. To achieve the goal to its full extent, the community has followed two complementary approaches for modeling and integrating phenotype data: a pre-composed and a post-composed representation (see Figure 2). The pre-composed approach treats each phenotype as an atomic entity, using individual expressions most suitable to general human understanding. For example, an ontology adopting this representation consists of concept definitions like ‘erythrocytopenia’ or ‘deficiency of red blood cells’ / ‘deficiency of erythrocytes’. These concepts are easily understood by humans and also facilitate computational analysis.

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.