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Deep phenotyping for precision medicine

Overview of attention for article published in Human Mutation, April 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#38 of 2,982)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
2 policy sources
twitter
5 X users
patent
1 patent

Citations

dimensions_citation
353 Dimensions

Readers on

mendeley
452 Mendeley
citeulike
1 CiteULike
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Title
Deep phenotyping for precision medicine
Published in
Human Mutation, April 2012
DOI 10.1002/humu.22080
Pubmed ID
Authors

Peter N. Robinson

Abstract

In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 452 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 3%
Germany 2 <1%
Spain 2 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Austria 1 <1%
Switzerland 1 <1%
Denmark 1 <1%
Luxembourg 1 <1%
Other 1 <1%
Unknown 429 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 93 21%
Student > Ph. D. Student 68 15%
Student > Master 42 9%
Other 34 8%
Student > Bachelor 34 8%
Other 86 19%
Unknown 95 21%
Readers by discipline Count As %
Medicine and Dentistry 92 20%
Biochemistry, Genetics and Molecular Biology 59 13%
Computer Science 47 10%
Agricultural and Biological Sciences 45 10%
Psychology 16 4%
Other 72 16%
Unknown 121 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2023.
All research outputs
#1,434,212
of 25,374,917 outputs
Outputs from Human Mutation
#38
of 2,982 outputs
Outputs of similar age
#7,682
of 173,665 outputs
Outputs of similar age from Human Mutation
#1
of 54 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,982 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 173,665 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.