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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
|
|---|---|
| Published in |
BMC Bioinformatics, August 2014
|
| DOI | 10.1186/1471-2105-15-291 |
| Pubmed ID | |
| Authors |
Hongjian Li, Kwong-Sak Leung, Man-Hon Wong, Pedro J Ballester |
| Abstract |
State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 17% |
| Norway | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Members of the public | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | <1% |
| Germany | 1 | <1% |
| Ecuador | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 97 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 17% |
| Student > Ph. D. Student | 15 | 15% |
| Student > Bachelor | 14 | 14% |
| Student > Master | 12 | 12% |
| Student > Doctoral Student | 10 | 10% |
| Other | 14 | 14% |
| Unknown | 20 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 18 | 18% |
| Agricultural and Biological Sciences | 16 | 16% |
| Computer Science | 12 | 12% |
| Biochemistry, Genetics and Molecular Biology | 8 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 5% |
| Other | 17 | 17% |
| Unknown | 26 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 01 October 2022.
All research outputs
#4,836,497
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#1,804
of 7,418 outputs
Outputs of similar age
#48,092
of 238,086 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 109 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 75% 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 238,086 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 79% of its contemporaries.
We're also able to compare this research output to 109 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 72% of its contemporaries.