↓ Skip to main content

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
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
6 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
90 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 90 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 85 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 17%
Researcher 15 17%
Student > Bachelor 13 14%
Student > Master 12 13%
Student > Doctoral Student 9 10%
Other 11 12%
Unknown 15 17%
Readers by discipline Count As %
Chemistry 15 17%
Agricultural and Biological Sciences 15 17%
Computer Science 11 12%
Biochemistry, Genetics and Molecular Biology 7 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Other 18 20%
Unknown 20 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 September 2014.
All research outputs
#4,303,749
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,963
of 5,420 outputs
Outputs of similar age
#55,093
of 200,846 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 6 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 62% 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 200,846 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 6 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.