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Mendeley readers
Attention Score in Context
| Title |
Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-72 |
| Pubmed ID | |
| Authors |
Masao Ueki, Gen Tamiya |
| Abstract |
Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. |
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 % |
|---|---|---|
| Japan | 1 | 33% |
| Italy | 1 | 33% |
| Australia | 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 1% |
| Malaysia | 1 | 1% |
| Germany | 1 | 1% |
| Belgium | 1 | 1% |
| Unknown | 63 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 30% |
| Researcher | 14 | 21% |
| Student > Master | 9 | 13% |
| Student > Bachelor | 4 | 6% |
| Student > Postgraduate | 4 | 6% |
| Other | 11 | 16% |
| Unknown | 5 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 19% |
| Computer Science | 13 | 19% |
| Biochemistry, Genetics and Molecular Biology | 8 | 12% |
| Mathematics | 6 | 9% |
| Medicine and Dentistry | 4 | 6% |
| Other | 13 | 19% |
| Unknown | 10 | 15% |
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 10 August 2012.
All research outputs
#13,360,809
of 22,664,644 outputs
Outputs from BMC Bioinformatics
#4,186
of 7,247 outputs
Outputs of similar age
#91,029
of 163,491 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 103 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 38th percentile – i.e., 38% 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 163,491 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 103 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.