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Mendeley readers
Attention Score in Context
| Title |
Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis
|
|---|---|
| Published in |
PLoS Computational Biology, March 2008
|
| DOI | 10.1371/journal.pcbi.1000043 |
| Pubmed ID | |
| Authors |
Ugo Ala, Rosario Michael Piro, Elena Grassi, Christian Damasco, Lorenzo Silengo, Martin Oti, Paolo Provero, Ferdinando Di Cunto |
| Abstract |
Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. |
Mendeley readers
The data shown below were compiled from readership statistics for 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 3% |
| United States | 3 | 2% |
| Italy | 3 | 2% |
| Germany | 2 | 1% |
| Australia | 1 | <1% |
| Czechia | 1 | <1% |
| Tunisia | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Greece | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 138 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 39 | 25% |
| Researcher | 34 | 22% |
| Student > Master | 17 | 11% |
| Professor > Associate Professor | 13 | 8% |
| Student > Bachelor | 10 | 6% |
| Other | 27 | 17% |
| Unknown | 16 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 72 | 46% |
| Computer Science | 21 | 13% |
| Biochemistry, Genetics and Molecular Biology | 19 | 12% |
| Medicine and Dentistry | 10 | 6% |
| Engineering | 3 | 2% |
| Other | 14 | 9% |
| Unknown | 17 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 20 December 2020.
All research outputs
#4,184,088
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#3,341
of 9,043 outputs
Outputs of similar age
#14,809
of 96,423 outputs
Outputs of similar age from PLoS Computational Biology
#16
of 44 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 63% 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 96,423 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 84% of its contemporaries.
We're also able to compare this research output to 44 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 63% of its contemporaries.