| Title |
A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data
|
|---|---|
| Published in |
BMC Systems Biology, March 2012
|
| DOI | 10.1186/1752-0509-6-15 |
| Pubmed ID | |
| Authors |
Min Li, Hanhui Zhang, Jian-xin Wang, Yi Pan |
| Abstract |
Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. |
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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| United Kingdom | 2 | 2% |
| Germany | 1 | <1% |
| Netherlands | 1 | <1% |
| Brazil | 1 | <1% |
| Hungary | 1 | <1% |
| Italy | 1 | <1% |
| India | 1 | <1% |
| Unknown | 110 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 24% |
| Researcher | 24 | 20% |
| Student > Bachelor | 13 | 11% |
| Student > Master | 10 | 8% |
| Professor | 6 | 5% |
| Other | 22 | 18% |
| Unknown | 16 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 41 | 34% |
| Computer Science | 26 | 22% |
| Biochemistry, Genetics and Molecular Biology | 16 | 13% |
| Medicine and Dentistry | 3 | 3% |
| Engineering | 3 | 3% |
| Other | 9 | 8% |
| Unknown | 22 | 18% |
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 14 March 2012.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from BMC Systems Biology
#827
of 1,132 outputs
Outputs of similar age
#132,474
of 169,164 outputs
Outputs of similar age from BMC Systems Biology
#8
of 9 outputs
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