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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automatic pathway building in biological association networks
|
|---|---|
| Published in |
BMC Bioinformatics, March 2006
|
| DOI | 10.1186/1471-2105-7-171 |
| Pubmed ID | |
| Authors |
Anton Yuryev, Zufar Mulyukov, Ekaterina Kotelnikova, Sergei Maslov, Sergei Egorov, Alexander Nikitin, Nikolai Daraselia, Ilya Mazo |
| Abstract |
Scientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined. |
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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| United States | 1 | 1% |
| Portugal | 1 | 1% |
| France | 1 | 1% |
| Unknown | 65 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 30% |
| Student > Ph. D. Student | 14 | 20% |
| Student > Master | 7 | 10% |
| Professor > Associate Professor | 5 | 7% |
| Professor | 5 | 7% |
| Other | 9 | 13% |
| Unknown | 8 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 43% |
| Computer Science | 10 | 14% |
| Medicine and Dentistry | 5 | 7% |
| Biochemistry, Genetics and Molecular Biology | 4 | 6% |
| Mathematics | 2 | 3% |
| Other | 8 | 12% |
| Unknown | 10 | 14% |
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 06 August 2013.
All research outputs
#18,342,133
of 22,715,151 outputs
Outputs from BMC Bioinformatics
#6,294
of 7,260 outputs
Outputs of similar age
#62,777
of 66,559 outputs
Outputs of similar age from BMC Bioinformatics
#52
of 59 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 66,559 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 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.