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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides.
|
|---|---|
| Chapter number | 4 |
| Book title |
Cell-Penetrating Peptides
|
| Published in |
Methods in molecular biology, January 2015
|
| DOI | 10.1007/978-1-4939-2806-4_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-2805-7, 978-1-4939-2806-4
|
| Authors |
Ankur Gautam, Kumardeep Chaudhary, Rahul Kumar, Gajendra Pal Singh Raghava |
| Editors |
Ülo Langel |
| Abstract |
Cell-penetrating peptides (CPPs) have proven their potential as versatile drug delivery vehicles. Last decade has witnessed an unprecedented growth in CPP-based research, demonstrating the potential of CPPs as therapeutic candidates. In the past, many in silico algorithms have been developed for the prediction and screening of CPPs, which expedites the CPP-based research. In silico screening/prediction of CPPs followed by experimental validation seems to be a reliable, less time-consuming, and cost-effective approach. This chapter describes the prediction, screening, and designing of novel efficient CPPs using "CellPPD," an in silico tool. |
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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 1% |
| Unknown | 76 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 17% |
| Student > Ph. D. Student | 12 | 16% |
| Student > Bachelor | 7 | 9% |
| Student > Master | 7 | 9% |
| Student > Doctoral Student | 3 | 4% |
| Other | 10 | 13% |
| Unknown | 25 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 16 | 21% |
| Agricultural and Biological Sciences | 16 | 21% |
| Chemistry | 5 | 6% |
| Immunology and Microbiology | 2 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 29 | 38% |
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 03 July 2016.
All research outputs
#15,379,760
of 22,880,230 outputs
Outputs from Methods in molecular biology
#5,351
of 13,132 outputs
Outputs of similar age
#209,202
of 353,344 outputs
Outputs of similar age from Methods in molecular biology
#345
of 997 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,132 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 353,344 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 997 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 57% of its contemporaries.