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Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

Overview of attention for article published in BMC Bioinformatics, August 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

policy
1 policy source
twitter
6 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
3 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 94 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 89 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 16%
Researcher 14 15%
Student > Bachelor 13 14%
Student > Master 12 13%
Student > Doctoral Student 10 11%
Other 13 14%
Unknown 17 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 17%
Chemistry 15 16%
Computer Science 12 13%
Biochemistry, Genetics and Molecular Biology 7 7%
Pharmacology, Toxicology and Pharmaceutical Science 5 5%
Other 17 18%
Unknown 22 23%

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
#4,497,302
of 22,366,177 outputs
Outputs from BMC Bioinformatics
#1,748
of 7,162 outputs
Outputs of similar age
#43,488
of 216,226 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 15 outputs
Altmetric has tracked 22,366,177 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 7,162 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 216,226 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 15 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 73% of its contemporaries.