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Mendeley readers
Attention Score in Context
| Title |
Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2013
|
| DOI | 10.1186/1758-2946-5-41 |
| Pubmed ID | |
| Authors |
Gerard JP van Westen, Remco F Swier, Jörg K Wegner, Adriaan P IJzerman, Herman WT van Vlijmen, Andreas Bender |
| Abstract |
While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 50% |
| Netherlands | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | <1% |
| Austria | 1 | <1% |
| Switzerland | 1 | <1% |
| Unknown | 138 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 32 | 23% |
| Student > Ph. D. Student | 31 | 22% |
| Student > Bachelor | 19 | 13% |
| Student > Master | 13 | 9% |
| Other | 6 | 4% |
| Other | 15 | 11% |
| Unknown | 25 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 26 | 18% |
| Biochemistry, Genetics and Molecular Biology | 23 | 16% |
| Agricultural and Biological Sciences | 19 | 13% |
| Computer Science | 14 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 10 | 7% |
| Other | 19 | 13% |
| Unknown | 30 | 21% |
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 13 December 2018.
All research outputs
#4,680,761
of 22,723,682 outputs
Outputs from Journal of Cheminformatics
#440
of 828 outputs
Outputs of similar age
#42,799
of 202,769 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 7 outputs
Altmetric has tracked 22,723,682 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 46th percentile – i.e., 46% 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 202,769 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 77% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.