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Mendeley readers
Attention Score in Context
| Title |
Markov clustering versus affinity propagation for the partitioning of protein interaction graphs
|
|---|---|
| Published in |
BMC Bioinformatics, March 2009
|
| DOI | 10.1186/1471-2105-10-99 |
| Pubmed ID | |
| Authors |
James Vlasblom, Shoshana J Wodak |
| Abstract |
Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs. |
Mendeley readers
The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| Germany | 5 | 2% |
| United Kingdom | 3 | 1% |
| France | 2 | <1% |
| Korea, Republic of | 1 | <1% |
| Italy | 1 | <1% |
| Kenya | 1 | <1% |
| Australia | 1 | <1% |
| Finland | 1 | <1% |
| Other | 9 | 4% |
| Unknown | 172 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 59 | 29% |
| Researcher | 45 | 22% |
| Student > Master | 27 | 13% |
| Student > Bachelor | 13 | 6% |
| Student > Doctoral Student | 9 | 4% |
| Other | 31 | 15% |
| Unknown | 20 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 59 | 29% |
| Computer Science | 55 | 27% |
| Biochemistry, Genetics and Molecular Biology | 23 | 11% |
| Engineering | 13 | 6% |
| Chemistry | 5 | 2% |
| Other | 25 | 12% |
| Unknown | 24 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 16 December 2021.
All research outputs
#7,427,950
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#3,025
of 7,255 outputs
Outputs of similar age
#32,466
of 92,373 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 29 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 92,373 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.