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GameRank: R package for feature selection and construction.

Carsten Henneges1, Joseph N Paulson1,2

  • 1Product Development Data Sciences, Genentech Inc., South San Francisco, CA 94080, USA.

Bioinformatics (Oxford, England)
|August 11, 2022
PubMed
Summary

We developed GameRank, a fast maximum likelihood-based feature selection algorithm, to efficiently build accurate predictive models for identifying high- and low-risk patients in clinical settings.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in healthcare

Background:

  • Predictive models are crucial in clinical settings for patient risk stratification.
  • Current combinatorial search algorithms for feature selection are often slow and may not find optimal solutions due to large search spaces.

Purpose of the Study:

  • To introduce a novel, efficient feature selection algorithm for building predictive models.
  • To address the limitations of existing methods in terms of speed and optimality.

Main Methods:

  • Developed GameRank, a maximum likelihood-based feature selection algorithm.
  • Implemented GameRank within an R package for broader usability.
  • The package includes functions for constructing calibrated and discriminative predictive models.

Main Results:

  • GameRank offers a quick and effective approach to feature selection.
  • The algorithm facilitates the development of high-performing predictive models.
  • Enables efficient identification of patient risk profiles.

Conclusions:

  • GameRank provides a computationally efficient solution for feature selection in predictive modeling.
  • The R package enhances the ability to build accurate clinical risk prediction tools.
  • This method can accelerate the deployment of predictive analytics in healthcare.