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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites
|
|---|---|
| Published in |
BMC Bioinformatics, August 2013
|
| DOI | 10.1186/1471-2105-14-247 |
| Pubmed ID | |
| Authors |
Liang Zou, Mang Wang, Yi Shen, Jie Liao, Ao Li, Minghui Wang |
| Abstract |
Dynamic protein phosphorylation is an essential regulatory mechanism in various organisms. In this capacity, it is involved in a multitude of signal transduction pathways. Kinase-specific phosphorylation data lay the foundation for reconstruction of signal transduction networks. For this reason, precise annotation of phosphorylated proteins is the first step toward simulating cell signaling pathways. However, the vast majority of kinase-specific phosphorylation data remain undiscovered and existing experimental methods and computational phosphorylation site (P-site) prediction tools have various limitations with respect to addressing this problem. |
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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Unknown | 51 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 19% |
| Researcher | 10 | 19% |
| Student > Master | 8 | 15% |
| Professor > Associate Professor | 5 | 9% |
| Student > Bachelor | 3 | 6% |
| Other | 5 | 9% |
| Unknown | 12 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 40% |
| Biochemistry, Genetics and Molecular Biology | 10 | 19% |
| Computer Science | 5 | 9% |
| Medicine and Dentistry | 2 | 4% |
| Immunology and Microbiology | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 13 | 25% |
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 August 2013.
All research outputs
#18,343,746
of 22,716,996 outputs
Outputs from BMC Bioinformatics
#6,294
of 7,260 outputs
Outputs of similar age
#147,400
of 197,044 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 71 outputs
Altmetric has tracked 22,716,996 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 7,260 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 5th percentile – i.e., 5% 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 197,044 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 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.