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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Performance of case-control rare copy number variation annotation in classification of autism
|
|---|---|
| Published in |
BMC Medical Genomics, January 2015
|
| DOI | 10.1186/1755-8794-8-s1-s7 |
| Pubmed ID | |
| Authors |
Worrawat Engchuan, Kiret Dhindsa, Anath C Lionel, Stephen W Scherer, Jonathan H Chan, Daniele Merico |
| Abstract |
A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Tunisia | 1 | 1% |
| Unknown | 75 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 21% |
| Student > Bachelor | 10 | 13% |
| Researcher | 9 | 12% |
| Student > Master | 9 | 12% |
| Student > Doctoral Student | 4 | 5% |
| Other | 12 | 16% |
| Unknown | 16 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 14% |
| Neuroscience | 10 | 13% |
| Agricultural and Biological Sciences | 8 | 11% |
| Medicine and Dentistry | 8 | 11% |
| Computer Science | 5 | 7% |
| Other | 13 | 17% |
| Unknown | 21 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 23 December 2021.
All research outputs
#5,525,197
of 22,745,803 outputs
Outputs from BMC Medical Genomics
#245
of 1,220 outputs
Outputs of similar age
#80,215
of 379,657 outputs
Outputs of similar age from BMC Medical Genomics
#10
of 41 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,220 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% 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 379,657 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.