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X Demographics
Mendeley readers
Attention Score in Context
| Title |
2D-Qsar for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope
|
|---|---|
| Published in |
Journal of Cheminformatics, November 2011
|
| DOI | 10.1186/1758-2946-3-50 |
| Pubmed ID | |
| Authors |
Tsutomu Osoda, Satoru Miyano |
| Abstract |
Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms. |
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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 40% |
| Student > Ph. D. Student | 3 | 20% |
| Student > Bachelor | 2 | 13% |
| Researcher | 1 | 7% |
| Professor > Associate Professor | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 27% |
| Biochemistry, Genetics and Molecular Biology | 4 | 27% |
| Computer Science | 2 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
| Chemistry | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 3 | 20% |
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 02 November 2011.
All research outputs
#15,238,442
of 22,656,971 outputs
Outputs from Journal of Cheminformatics
#743
of 825 outputs
Outputs of similar age
#96,461
of 141,797 outputs
Outputs of similar age from Journal of Cheminformatics
#23
of 23 outputs
Altmetric has tracked 22,656,971 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 825 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. 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 141,797 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 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.