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Mendeley readers
Attention Score in Context
| Title |
Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks
|
|---|---|
| Published in |
BMC Systems Biology, July 2012
|
| DOI | 10.1186/1752-0509-6-87 |
| Pubmed ID | |
| Authors |
Wei Peng, Jianxin Wang, Weiping Wang, Qing Liu, Fang-Xiang Wu, Yi Pan |
| Abstract |
Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Hungary | 1 | 2% |
| Netherlands | 1 | 2% |
| Germany | 1 | 2% |
| Mexico | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 45 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 23% |
| Researcher | 7 | 13% |
| Student > Bachelor | 6 | 12% |
| Student > Master | 6 | 12% |
| Professor > Associate Professor | 4 | 8% |
| Other | 7 | 13% |
| Unknown | 10 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 31% |
| Agricultural and Biological Sciences | 13 | 25% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Engineering | 2 | 4% |
| Mathematics | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 11 | 21% |
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 August 2012.
All research outputs
#19,015,492
of 23,577,654 outputs
Outputs from BMC Systems Biology
#833
of 1,139 outputs
Outputs of similar age
#127,120
of 165,192 outputs
Outputs of similar age from BMC Systems Biology
#29
of 38 outputs
Altmetric has tracked 23,577,654 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 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% 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 165,192 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.