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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis
|
|---|---|
| Published in |
BioData Mining, February 2013
|
| DOI | 10.1186/1756-0381-6-4 |
| Pubmed ID | |
| Authors |
Ryan L Collins, Ting Hu, Christian Wejse, Giorgio Sirugo, Scott M Williams, Jason H Moore |
| Abstract |
Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Germany | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 59 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 29% |
| Student > Master | 13 | 21% |
| Researcher | 8 | 13% |
| Student > Bachelor | 4 | 6% |
| Student > Postgraduate | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 9 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 26% |
| Computer Science | 10 | 16% |
| Biochemistry, Genetics and Molecular Biology | 9 | 15% |
| Medicine and Dentistry | 5 | 8% |
| Immunology and Microbiology | 3 | 5% |
| Other | 8 | 13% |
| Unknown | 11 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 14 May 2017.
All research outputs
#6,922,550
of 22,699,621 outputs
Outputs from BioData Mining
#148
of 307 outputs
Outputs of similar age
#57,969
of 192,548 outputs
Outputs of similar age from BioData Mining
#3
of 7 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 50% of its peers.
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 192,548 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.