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Modeling the Autism Spectrum Disorder Phenotype

Overview of attention for article published in Neuroinformatics, October 2013
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Title
Modeling the Autism Spectrum Disorder Phenotype
Published in
Neuroinformatics, October 2013
DOI 10.1007/s12021-013-9211-4
Pubmed ID
Authors

Alexa T. McCray, Philip Trevvett, H. Robert Frost

Abstract

Autism Spectrum Disorder (ASD) is highly heritable, and although there has been active research in an attempt to discover the genetic factors underlying ASD, diagnosis still depends heavily on behavioral assessments. Recently, several large-scale initiatives, including those of the Autism Consortium, have contributed to the collection of extensive information from families affected by ASD. Our goal was to develop an ontology that can be used 1) to provide improved access to the data collected by those who study ASD and other neurodevelopmental disorders, and 2) to assess and compare the characteristics of the instruments that are used in the assessment of ASD. We analyzed two dozen instruments used to assess ASD, studying the nature of the questions asked and items assessed, the method of delivery, and the overall scope of the content. These data together with the extensive literature on ASD contributed to our iterative development of an ASD phenotype ontology. The final ontology comprises 283 concepts distributed across three high-level classes, 'Personal Traits', 'Social Competence', and 'Medical History'. The ontology is fully integrated with the Autism Consortium database, allowing researchers to pose ontology-based questions. The ontology also allows researchers to assess the degree of overlap among a set of candidate instruments according to several objective criteria. The ASD phenotype ontology has promise for use in research settings where extensive phenotypic data have been collected, allowing a concept-based approach to identifying behavioral features of importance and for correlating these with genotypic data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Canada 1 <1%
Unknown 108 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 21%
Student > Ph. D. Student 16 14%
Student > Master 16 14%
Student > Doctoral Student 10 9%
Student > Bachelor 7 6%
Other 20 18%
Unknown 19 17%
Readers by discipline Count As %
Psychology 26 23%
Computer Science 13 12%
Agricultural and Biological Sciences 12 11%
Neuroscience 11 10%
Medicine and Dentistry 5 5%
Other 19 17%
Unknown 25 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 June 2015.
All research outputs
#15,351,826
of 25,654,806 outputs
Outputs from Neuroinformatics
#222
of 432 outputs
Outputs of similar age
#122,519
of 226,121 outputs
Outputs of similar age from Neuroinformatics
#3
of 4 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 432 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.