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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting HLA genotypes using unphased and flanking single-nucleotide polymorphisms in Han Chinese population
|
|---|---|
| Published in |
BMC Genomics, January 2014
|
| DOI | 10.1186/1471-2164-15-81 |
| Pubmed ID | |
| Authors |
Ai-Ru Hsieh, Su-Wei Chang, Pei-Lung Chen, Chen-Chung Chu, Ching-Lin Hsiao, Wei-Shiung Yang, Chien-Ching Chang, Jer-Yuarn Wu, Yuan-Tsong Chen, Tien-Chun Chang, Cathy SJ Fann |
| Abstract |
Genetic variation associated with human leukocyte antigen (HLA) genes has immunological functions and is associated with autoimmune diseases. To date, large-scale studies involving classical HLA genes have been limited by time-consuming and expensive HLA-typing technologies. To reduce these costs, single-nucleotide polymorphisms (SNPs) have been used to predict HLA-allele types. Although HLA allelic distributions differ among populations, most prediction model of HLA genes are based on Caucasian samples, with few reported studies involving non-Caucasians. |
X Demographics
The data shown below were collected from the profile of 1 X user 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 3% |
| Netherlands | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 31 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 21% |
| Student > Ph. D. Student | 6 | 18% |
| Other | 5 | 15% |
| Student > Bachelor | 3 | 9% |
| Student > Master | 3 | 9% |
| Other | 4 | 12% |
| Unknown | 6 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 26% |
| Agricultural and Biological Sciences | 9 | 26% |
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Computer Science | 3 | 9% |
| Unspecified | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 04 February 2014.
All research outputs
#18,369,403
of 22,751,628 outputs
Outputs from BMC Genomics
#8,164
of 10,636 outputs
Outputs of similar age
#230,354
of 308,152 outputs
Outputs of similar age from BMC Genomics
#336
of 440 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,636 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 308,152 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 440 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.