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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detection for gene-gene co-association via kernel canonical correlation analysis
|
|---|---|
| Published in |
BMC Genomic Data, October 2012
|
| DOI | 10.1186/1471-2156-13-83 |
| Pubmed ID | |
| Authors |
Zhongshang Yuan, Qingsong Gao, Yungang He, Xiaoshuai Zhang, Fangyu Li, Jinghua Zhao, Fuzhong Xue |
| Abstract |
Currently, most methods for detecting gene-gene interaction (GGI) in genomewide association studies (GWASs) are limited in their use of single nucleotide polymorphism (SNP) as the unit of association. One way to address this drawback is to consider higher level units such as genes or regions in the analysis. Earlier we proposed a statistic based on canonical correlations (CCU) as a gene-based method for detecting gene-gene co-association. However, it can only capture linear relationship and not nonlinear correlation between genes. We therefore proposed a counterpart (KCCU) based on kernel canonical correlation analysis (KCCA). |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 24% |
| Professor > Associate Professor | 4 | 16% |
| Student > Master | 3 | 12% |
| Researcher | 3 | 12% |
| Student > Doctoral Student | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 7 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 24% |
| Agricultural and Biological Sciences | 3 | 12% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Engineering | 2 | 8% |
| Medicine and Dentistry | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 10 | 40% |
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 21 October 2012.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from BMC Genomic Data
#861
of 1,204 outputs
Outputs of similar age
#149,699
of 192,244 outputs
Outputs of similar age from BMC Genomic Data
#13
of 17 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 16th percentile – i.e., 16% 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 192,244 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.