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X Demographics
Mendeley readers
Attention Score in Context
| Title |
VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses
|
|---|---|
| Published in |
Journal of Translational Medicine, December 2013
|
| DOI | 10.1186/1479-5876-11-305 |
| Pubmed ID | |
| Authors |
Abid Qureshi, Nishant Thakur, Manoj Kumar |
| Abstract |
Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed "VIRsiRNApred" a support vector machine (SVM) based method for predicting the efficacy of viral siRNA. |
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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 14 | 21% |
| Researcher | 11 | 16% |
| Student > Ph. D. Student | 10 | 15% |
| Student > Master | 10 | 15% |
| Student > Doctoral Student | 2 | 3% |
| Other | 8 | 12% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 15% |
| Agricultural and Biological Sciences | 9 | 13% |
| Medicine and Dentistry | 9 | 13% |
| Computer Science | 7 | 10% |
| Nursing and Health Professions | 4 | 6% |
| Other | 12 | 18% |
| Unknown | 16 | 24% |
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 12 December 2013.
All research outputs
#20,213,623
of 22,736,112 outputs
Outputs from Journal of Translational Medicine
#3,303
of 3,974 outputs
Outputs of similar age
#267,066
of 307,039 outputs
Outputs of similar age from Journal of Translational Medicine
#79
of 115 outputs
Altmetric has tracked 22,736,112 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 3,974 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 1st percentile – i.e., 1% 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 307,039 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 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.