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Mendeley readers
Attention Score in Context
| Title |
ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-389 |
| Pubmed ID | |
| Authors |
Fantine Mordelet, Jean-Philippe Vert |
| Abstract |
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. |
Mendeley readers
The data shown below were compiled from readership statistics for 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 3% |
| United States | 4 | 3% |
| United Kingdom | 2 | 1% |
| Turkey | 1 | <1% |
| Brazil | 1 | <1% |
| France | 1 | <1% |
| Slovenia | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Unknown | 126 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 25% |
| Researcher | 22 | 16% |
| Student > Master | 19 | 13% |
| Student > Bachelor | 8 | 6% |
| Professor > Associate Professor | 7 | 5% |
| Other | 28 | 20% |
| Unknown | 22 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 48 | 34% |
| Agricultural and Biological Sciences | 22 | 16% |
| Biochemistry, Genetics and Molecular Biology | 18 | 13% |
| Medicine and Dentistry | 9 | 6% |
| Engineering | 7 | 5% |
| Other | 14 | 10% |
| Unknown | 23 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 2011.
All research outputs
#4,861,928
of 24,396,012 outputs
Outputs from BMC Bioinformatics
#1,745
of 7,528 outputs
Outputs of similar age
#26,501
of 137,362 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 83 outputs
Altmetric has tracked 24,396,012 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,528 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 76% 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 137,362 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.