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Mendeley readers
Attention Score in Context
| Title |
Stability SCAD: a powerful approach to detect interactions in large-scale genomic study
|
|---|---|
| Published in |
BMC Bioinformatics, March 2014
|
| DOI | 10.1186/1471-2105-15-62 |
| Pubmed ID | |
| Authors |
Jianwei Gou, Yang Zhao, Yongyue Wei, Chen Wu, Ruyang Zhang, Yongyong Qiu, Ping Zeng, Wen Tan, Dianke Yu, Tangchun Wu, Zhibin Hu, Dongxin Lin, Hongbing Shen, Feng Chen |
| Abstract |
Evidence suggests that common complex diseases may be partially due to SNP-SNP interactions, but such detection is yet to be fully established in a high-dimensional small-sample (small-n-large-p) study. A number of penalized regression techniques are gaining popularity within the statistical community, and are now being applied to detect interactions. These techniques tend to be over-fitting, and are prone to false positives. The recently developed stability least absolute shrinkage and selection operator (SLASSO) has been used to control family-wise error rate, but often at the expense of power (and thus false negative results). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 50% |
| Norway | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 5% |
| Sweden | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 18 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 38% |
| Researcher | 6 | 29% |
| Student > Doctoral Student | 2 | 10% |
| Professor > Associate Professor | 2 | 10% |
| Student > Postgraduate | 2 | 10% |
| Other | 0 | 0% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 19% |
| Computer Science | 4 | 19% |
| Biochemistry, Genetics and Molecular Biology | 3 | 14% |
| Mathematics | 2 | 10% |
| Engineering | 2 | 10% |
| Other | 4 | 19% |
| Unknown | 2 | 10% |
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 08 March 2014.
All research outputs
#17,713,929
of 22,745,803 outputs
Outputs from BMC Bioinformatics
#5,925
of 7,268 outputs
Outputs of similar age
#154,342
of 222,148 outputs
Outputs of similar age from BMC Bioinformatics
#79
of 104 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 222,148 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.