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Mendeley readers
Attention Score in Context
| Title |
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function
|
|---|---|
| Published in |
Genome Biology, June 2008
|
| DOI | 10.1186/gb-2008-9-s1-s4 |
| Pubmed ID | |
| Authors |
Sara Mostafavi, Debajyoti Ray, David Warde-Farley, Chris Grouios, Quaid Morris |
| Abstract |
Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. |
Mendeley readers
The data shown below were compiled from readership statistics for 589 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 20 | 3% |
| United Kingdom | 10 | 2% |
| Canada | 4 | <1% |
| Brazil | 4 | <1% |
| Italy | 3 | <1% |
| France | 2 | <1% |
| Switzerland | 2 | <1% |
| Sweden | 2 | <1% |
| Germany | 2 | <1% |
| Other | 14 | 2% |
| Unknown | 526 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 187 | 32% |
| Researcher | 113 | 19% |
| Student > Bachelor | 60 | 10% |
| Student > Master | 53 | 9% |
| Professor | 26 | 4% |
| Other | 82 | 14% |
| Unknown | 68 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 215 | 37% |
| Biochemistry, Genetics and Molecular Biology | 100 | 17% |
| Computer Science | 78 | 13% |
| Medicine and Dentistry | 38 | 6% |
| Engineering | 14 | 2% |
| Other | 58 | 10% |
| Unknown | 86 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 22. 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 15 January 2022.
All research outputs
#1,710,973
of 25,373,627 outputs
Outputs from Genome Biology
#1,398
of 4,467 outputs
Outputs of similar age
#4,267
of 96,206 outputs
Outputs of similar age from Genome Biology
#6
of 43 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 68% 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 96,206 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.