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X Demographics
Mendeley readers
Attention Score in Context
| Title |
In silico approaches for designing highly effective cell penetrating peptides
|
|---|---|
| Published in |
Journal of Translational Medicine, March 2013
|
| DOI | 10.1186/1479-5876-11-74 |
| Pubmed ID | |
| Authors |
Ankur Gautam, Kumardeep Chaudhary, Rahul Kumar, Arun Sharma, Pallavi Kapoor, Atul Tyagi, Open source drug discovery consortium, Gajendra P S Raghava |
| Abstract |
Cell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 229 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | <1% |
| Mexico | 1 | <1% |
| United States | 1 | <1% |
| Colombia | 1 | <1% |
| Unknown | 224 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 50 | 22% |
| Researcher | 33 | 14% |
| Student > Master | 29 | 13% |
| Student > Bachelor | 27 | 12% |
| Student > Doctoral Student | 10 | 4% |
| Other | 21 | 9% |
| Unknown | 59 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 51 | 22% |
| Biochemistry, Genetics and Molecular Biology | 50 | 22% |
| Chemistry | 20 | 9% |
| Computer Science | 12 | 5% |
| Immunology and Microbiology | 8 | 3% |
| Other | 23 | 10% |
| Unknown | 65 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 January 2021.
All research outputs
#5,626,767
of 22,714,025 outputs
Outputs from Journal of Translational Medicine
#863
of 3,973 outputs
Outputs of similar age
#46,866
of 197,451 outputs
Outputs of similar age from Journal of Translational Medicine
#15
of 58 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,973 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done well, scoring higher than 78% 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 197,451 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 58 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 74% of its contemporaries.