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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting overlapping protein complexes based on a generative model with functional and topological properties
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-186 |
| Pubmed ID | |
| Authors |
Xiao-Fei Zhang, Dao-Qing Dai, Le Ou-Yang, Hong Yan |
| Abstract |
Identification of protein complexes can help us get a better understanding of cellular mechanism. With the increasing availability of large-scale protein-protein interaction (PPI) data, numerous computational approaches have been proposed to detect complexes from the PPI networks. However, most of the current approaches do not consider overlaps among complexes or functional annotation information of individual proteins. Therefore, they might not be able to reflect the biological reality faithfully or make full use of the available domain-specific knowledge. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Luxembourg | 1 | 4% |
| Australia | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 6 | 24% |
| Lecturer | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Lecturer > Senior Lecturer | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 5 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 36% |
| Biochemistry, Genetics and Molecular Biology | 4 | 16% |
| Agricultural and Biological Sciences | 3 | 12% |
| Mathematics | 2 | 8% |
| Unspecified | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 5 | 20% |
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 13 June 2014.
All research outputs
#20,231,392
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#6,844
of 7,272 outputs
Outputs of similar age
#193,302
of 228,650 outputs
Outputs of similar age from BMC Bioinformatics
#142
of 153 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 1st percentile – i.e., 1% 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 228,650 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 153 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.