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Mendeley readers
Attention Score in Context
| Title |
Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines
|
|---|---|
| Published in |
BMC Genomics, March 2014
|
| DOI | 10.1186/1471-2164-15-198 |
| Pubmed ID | |
| Authors |
Shurjo K Sen, Jennifer J Barb, Praveen F Cherukuri, David S Accame, Abdel G Elkahloun, Larry N Singh, Shih-Queen Lee-Lin, NISC Comparative Sequencing Program, Frank D Kolodgie, Qi Cheng, XiaoQing Zhao, Marcus Y Chen, Andrew E Arai, Eric D Green, James C Mullikin, Peter J Munson, Leslie G Biesecker |
| Abstract |
Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics. Here, we used RNA-Seq to study the transcriptomes of matched coronary artery disease cases and controls in the ClinSeq® study, using cell lines as tissue surrogates. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 50% |
| Ireland | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 1 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 4% |
| Unknown | 25 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 23% |
| Researcher | 5 | 19% |
| Student > Doctoral Student | 3 | 12% |
| Student > Master | 2 | 8% |
| Student > Bachelor | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 7 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 23% |
| Agricultural and Biological Sciences | 5 | 19% |
| Medicine and Dentistry | 5 | 19% |
| Computer Science | 2 | 8% |
| Business, Management and Accounting | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 7 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 09 March 2015.
All research outputs
#13,671,566
of 24,224,854 outputs
Outputs from BMC Genomics
#4,618
of 10,925 outputs
Outputs of similar age
#106,722
of 225,512 outputs
Outputs of similar age from BMC Genomics
#52
of 151 outputs
Altmetric has tracked 24,224,854 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,925 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 56% 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 225,512 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 151 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 64% of its contemporaries.