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Mendeley readers
Attention Score in Context
| Title |
Identifying protein complexes based on density and modularity in protein-protein interaction network
|
|---|---|
| Published in |
BMC Systems Biology, October 2013
|
| DOI | 10.1186/1752-0509-7-s4-s12 |
| Pubmed ID | |
| Authors |
Jun Ren, Jianxin Wang, Min Li, Lusheng Wang |
| Abstract |
Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. |
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 % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 25% |
| Student > Master | 3 | 25% |
| Researcher | 2 | 17% |
| Professor > Associate Professor | 1 | 8% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 42% |
| Mathematics | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Unknown | 5 | 42% |
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 08 November 2014.
All research outputs
#20,242,136
of 22,769,322 outputs
Outputs from BMC Systems Biology
#1,009
of 1,142 outputs
Outputs of similar age
#185,040
of 212,180 outputs
Outputs of similar age from BMC Systems Biology
#23
of 26 outputs
Altmetric has tracked 22,769,322 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 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 26 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.