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

Georgia Tsiliki1, Cristian R Munteanu2,3, Jose A Seoane4

  • 1School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780 Athens, Greece.

Journal of Cheminformatics
|September 18, 2015
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Summary
This summary is machine-generated.

RRegrs is a new R package that streamlines predictive model development by testing multiple regression and validation methods. This open-source tool enhances model reproducibility and efficiency for cheminformatics and QSAR applications.

Keywords:
Caret-based toolMultiple regressionQSARR package

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Area of Science:

  • Computational chemistry and cheminformatics
  • Bioinformatics
  • Quantitative Structure-Activity Relationship (QSAR) modeling

Background:

  • Predictive regression modeling involves numerous choices impacting accuracy and reproducibility.
  • Standardization is needed in cheminformatics and bioinformatics to aid model selection and development.
  • A user-friendly, integrated tool is valuable for testing various regression and validation methods.

Purpose of the Study:

  • To develop an integrated framework for creating and validating multiple regression models.
  • To provide a standardized, automated procedure for assessing predictive model performance.
  • To create an open-source R package for accessible and adaptable predictive modeling.

Main Methods:

  • The RRegrs framework integrates ten regression methods (e.g., Multiple Linear Regression, Lasso, SVM) with cross-validation (10-fold, Leave-One-Out).
  • It automates model creation, validation, and generates standardized reports.
  • The package reuses and extends functionalities from the caret package in R.

Main Results:

  • RRegrs was validated on five diverse datasets, demonstrating its universality.
  • Its application to cheminformatics and QSAR showed efficient performance on proteomics, nano-metal oxides, and aquatic toxicity data.
  • Models generated by RRegrs exhibited equal or superior performance compared to original publications.

Conclusions:

  • RRegrs offers a validated, automated framework for predictive model selection.
  • Its adaptability and performance make it suitable for initial in silico screening and model exploration.
  • The open-source R package facilitates broader adoption and integration into complex screening applications.