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Mendeley readers
Attention Score in Context
| Title |
A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data
|
|---|---|
| Published in |
Journal of Translational Medicine, September 2009
|
| DOI | 10.1186/1479-5876-7-81 |
| Pubmed ID | |
| Authors |
Lung-Cheng Huang, Sen-Yen Hsu, Eugene Lin |
| Abstract |
In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs). |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 1% |
| Italy | 1 | 1% |
| Australia | 1 | 1% |
| Unknown | 72 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 19% |
| Researcher | 11 | 15% |
| Student > Master | 9 | 12% |
| Student > Bachelor | 8 | 11% |
| Other | 5 | 7% |
| Other | 18 | 24% |
| Unknown | 10 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 20% |
| Computer Science | 12 | 16% |
| Medicine and Dentistry | 12 | 16% |
| Biochemistry, Genetics and Molecular Biology | 6 | 8% |
| Nursing and Health Professions | 2 | 3% |
| Other | 11 | 15% |
| Unknown | 17 | 23% |
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 04 March 2015.
All research outputs
#20,263,155
of 22,793,427 outputs
Outputs from Journal of Translational Medicine
#3,309
of 3,988 outputs
Outputs of similar age
#88,928
of 92,951 outputs
Outputs of similar age from Journal of Translational Medicine
#10
of 11 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,988 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.