↓ Skip to main content

Proteochemometric modeling in a Bayesian framework

Overview of attention for article published in Journal of Cheminformatics, June 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
76 Mendeley
citeulike
2 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
Proteochemometric modeling in a Bayesian framework
Published in
Journal of Cheminformatics, June 2014
DOI 10.1186/1758-2946-6-35
Pubmed ID
Authors

Isidro Cortes-Ciriano, Gerard JP van Westen, Eelke Bart Lenselink, Daniel S Murrell, Andreas Bender, Thérèse Malliavin

Abstract

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model. In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%. GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with [Formula: see text] values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Italy 1 1%
Germany 1 1%
Taiwan 1 1%
Unknown 71 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 26%
Student > Ph. D. Student 14 18%
Student > Bachelor 10 13%
Student > Master 9 12%
Student > Doctoral Student 8 11%
Other 8 11%
Unknown 7 9%
Readers by discipline Count As %
Chemistry 21 28%
Agricultural and Biological Sciences 16 21%
Computer Science 13 17%
Biochemistry, Genetics and Molecular Biology 5 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 9 12%
Unknown 8 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 31 July 2014.
All research outputs
#1,532,971
of 13,132,528 outputs
Outputs from Journal of Cheminformatics
#179
of 529 outputs
Outputs of similar age
#25,204
of 192,249 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,132,528 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 529 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 65% 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 192,249 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 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them