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RRegrs: an R package for computer-aided model selection with multiple regression models

Overview of attention for article published in Journal of Cheminformatics, September 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 466)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
2 blogs
twitter
19 tweeters
facebook
1 Facebook page
googleplus
3 Google+ users

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
7 CiteULike
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Title
RRegrs: an R package for computer-aided model selection with multiple regression models
Published in
Journal of Cheminformatics, September 2015
DOI 10.1186/s13321-015-0094-2
Pubmed ID
Authors

Georgia Tsiliki, Cristian R. Munteanu, Jose A. Seoane, Carlos Fernandez-Lozano, Haralambos Sarimveis, Egon L. Willighagen

Abstract

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Netherlands 1 1%
Bulgaria 1 1%
Brazil 1 1%
Nigeria 1 1%
Unknown 77 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 29%
Student > Ph. D. Student 18 22%
Student > Bachelor 7 9%
Student > Master 6 7%
Professor > Associate Professor 5 6%
Other 17 21%
Unknown 5 6%
Readers by discipline Count As %
Computer Science 20 24%
Agricultural and Biological Sciences 15 18%
Engineering 10 12%
Chemistry 7 9%
Medicine and Dentistry 5 6%
Other 18 22%
Unknown 7 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 06 November 2016.
All research outputs
#388,092
of 11,920,364 outputs
Outputs from Journal of Cheminformatics
#16
of 466 outputs
Outputs of similar age
#12,717
of 240,112 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 14 outputs
Altmetric has tracked 11,920,364 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 466 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has done particularly well, scoring higher than 96% 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 240,112 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.