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Attention Score in Context
Title |
Exome-assistant: a rapid and easy detection of disease-related genes and genetic variations from exome sequencing
|
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Published in |
BMC Genomics, December 2012
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DOI | 10.1186/1471-2164-13-692 |
Pubmed ID | |
Authors |
Qi Liu, Enjian Shen, Qingjie Min, Xueying Li, Xin Wang, Xianfeng Li, Zhong Sheng Sun, Jinyu Wu |
Abstract |
Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits. Compared with whole-genome sequencing of complex samples, exome sequencing serves as an alternative option because of its dramatically reduced cost. In fact, exome sequencing has been successfully applied to identify the cause of several Mendelian disorders, such as Miller and Schinzel-Giedio syndrome. However, there remain great challenges in handling the huge data generated by exome sequencing and in identifying potential disease-related genetic variations. |
X Demographics
The data shown below were collected from the profiles of 12 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 Kingdom | 3 | 25% |
United States | 2 | 17% |
Germany | 1 | 8% |
Sweden | 1 | 8% |
Montenegro | 1 | 8% |
Unknown | 4 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 8 | 67% |
Members of the public | 3 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 3% |
United States | 2 | 3% |
Turkey | 1 | 2% |
India | 1 | 2% |
France | 1 | 2% |
China | 1 | 2% |
Sweden | 1 | 2% |
Unknown | 56 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 32% |
Student > Ph. D. Student | 14 | 22% |
Student > Master | 6 | 9% |
Student > Postgraduate | 5 | 8% |
Other | 5 | 8% |
Other | 11 | 17% |
Unknown | 3 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 32 | 49% |
Biochemistry, Genetics and Molecular Biology | 10 | 15% |
Medicine and Dentistry | 8 | 12% |
Computer Science | 5 | 8% |
Engineering | 3 | 5% |
Other | 4 | 6% |
Unknown | 3 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 August 2016.
All research outputs
#3,158,166
of 22,689,790 outputs
Outputs from BMC Genomics
#1,204
of 10,617 outputs
Outputs of similar age
#33,146
of 278,718 outputs
Outputs of similar age from BMC Genomics
#55
of 380 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,617 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 88% 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 278,718 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 88% of its contemporaries.
We're also able to compare this research output to 380 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.