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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Gene set analysis using variance component tests
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-210 |
| Pubmed ID | |
| Authors |
Yen-Tsung Huang, Xihong Lin |
| Abstract |
Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. |
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% |
| Norway | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| Brazil | 1 | 2% |
| Sweden | 1 | 2% |
| Argentina | 1 | 2% |
| Denmark | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 50 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 36% |
| Researcher | 19 | 34% |
| Professor > Associate Professor | 4 | 7% |
| Student > Master | 4 | 7% |
| Professor | 2 | 4% |
| Other | 7 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 43% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Computer Science | 6 | 11% |
| Medicine and Dentistry | 6 | 11% |
| Mathematics | 5 | 9% |
| Other | 4 | 7% |
| Unknown | 4 | 7% |
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 28 June 2013.
All research outputs
#15,379,994
of 24,846,849 outputs
Outputs from BMC Bioinformatics
#4,684
of 7,595 outputs
Outputs of similar age
#113,133
of 200,592 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 88 outputs
Altmetric has tracked 24,846,849 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,595 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 35th percentile – i.e., 35% 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 200,592 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 88 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.