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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-s16-s10 |
| Pubmed ID | |
| Authors |
Kai-Yao Huang, Cheng-Tsung Lu, Neil Arvin Bretaña, Tzong-Yi Lee, Tzu-Hao Chang |
| Abstract |
The phosphorylation of virus proteins by host kinases is linked to viral replication. This leads to an inhibition of normal host-cell functions. Further elucidation of phosphorylation in virus proteins is required in order to aid in drug design and treatment. However, only a few studies have investigated substrate motifs in identifying virus phosphorylation sites. Additionally, existing bioinformatics tool do not consider potential host kinases that may initiate the phosphorylation of a virus protein. |
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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 1 | 14% |
| Student > Ph. D. Student | 1 | 14% |
| Student > Bachelor | 1 | 14% |
| Student > Doctoral Student | 1 | 14% |
| Unknown | 3 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 1 | 14% |
| Social Sciences | 1 | 14% |
| Medicine and Dentistry | 1 | 14% |
| Unknown | 4 | 57% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 04 November 2016.
All research outputs
#6,946,410
of 22,778,347 outputs
Outputs from BMC Bioinformatics
#2,679
of 7,276 outputs
Outputs of similar age
#64,311
of 212,145 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 116 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 61% of its peers.
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 212,145 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.