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.


Related in: MedlinePlus

The increasing amount of data made available over the course of the past years have rendered manual phenotype curation impractical. While automating the process is in principle the only viable solution, it possesses its own plethora of technical challenges. These include, among others: (i) boundary detection, i.e. identifying the exact span of text that represents a phenotype candidate; (ii) disambiguation and alignment, subject to the desired level of granularity and the underlying knowledge source; and (iii) interpretation, which covers lack of context, hedging or negation.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

bbv083-F3: The increasing amount of data made available over the course of the past years have rendered manual phenotype curation impractical. While automating the process is in principle the only viable solution, it possesses its own plethora of technical challenges. These include, among others: (i) boundary detection, i.e. identifying the exact span of text that represents a phenotype candidate; (ii) disambiguation and alignment, subject to the desired level of granularity and the underlying knowledge source; and (iii) interpretation, which covers lack of context, hedging or negation.

Mentions: Acquisition involves the collection and storage of phenotype information from various resources (see Figure 3), such as OMIM or OrphaNet, a rare disease database [30]. While some of these resources are mainly built through manual curation, e.g. MGD, others rely already on (semi-)automated preprocessing to enhance curator throughput. For example, PharmGKB [46] uses an automated classification system to determine relevant publications and extract gene–drug relationships that are then provided to curators for verification [31, 47].Figure 3.


The digital revolution in phenotyping
The increasing amount of data made available over the course of the past years have rendered manual phenotype curation impractical. While automating the process is in principle the only viable solution, it possesses its own plethora of technical challenges. These include, among others: (i) boundary detection, i.e. identifying the exact span of text that represents a phenotype candidate; (ii) disambiguation and alignment, subject to the desired level of granularity and the underlying knowledge source; and (iii) interpretation, which covers lack of context, hedging or negation.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

bbv083-F3: The increasing amount of data made available over the course of the past years have rendered manual phenotype curation impractical. While automating the process is in principle the only viable solution, it possesses its own plethora of technical challenges. These include, among others: (i) boundary detection, i.e. identifying the exact span of text that represents a phenotype candidate; (ii) disambiguation and alignment, subject to the desired level of granularity and the underlying knowledge source; and (iii) interpretation, which covers lack of context, hedging or negation.
Mentions: Acquisition involves the collection and storage of phenotype information from various resources (see Figure 3), such as OMIM or OrphaNet, a rare disease database [30]. While some of these resources are mainly built through manual curation, e.g. MGD, others rely already on (semi-)automated preprocessing to enhance curator throughput. For example, PharmGKB [46] uses an automated classification system to determine relevant publications and extract gene–drug relationships that are then provided to curators for verification [31, 47].Figure 3.

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.


Related in: MedlinePlus