Henneges C, Paulson JN. GameRank: R package for feature selection and construction.
Bioinformatics 2022;
38:4840-4842. [PMID:
35951761 PMCID:
PMC9563696 DOI:
10.1093/bioinformatics/btac552]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION
Building calibrated and discriminating predictive models can be developed through the direct optimization of model performance metrics with combinatorial search algorithms. Often, predictive algorithms are desired in clinical settings to identify patients that may be high and low risk. However, due to the large combinatorial search space, these algorithms are slow and do not guarantee global optimality of their selection.
RESULTS
Here we present a novel and quick maximum-likelihood-based feature selection algorithm, named GameRank. The method is implemented into an R package composed of additional functions to build calibrated and discriminative predictive models.
AVAILABILITY
GameRank is available at https://github.com/Genentech/GameRank and released under the MIT License.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Collapse