Related Experiment Video
Updated: Aug 19, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Comparison of commercially available genetic algorithms: gas as variable selection tool
Sabine Schefzick1, Mary Bradley
1Pfizer Global Research and Development, Discovery Technologies, Ann Arbor Laboratories, 2800 Plymouth Road, Ann Arbor, MI 48105, USA. sabine.schefzick@pfizer.com
Abstract:
Many commercially available software programs claim similar efficiency and accuracy as variable selection tools. Genetic algorithms are commonly used variable selection methods where most relevant variables can be differentiated from 'less important' variables using evolutionary computing techniques. However, different vendors offer several algorithms, and the puzzling question is: which one is the appropriate method of choice? In this study, several genetic algorithm tools (e.g. GFA from Cerius2, QuaSAR-Evolution from MOE and Partek's genetic algorithm) were compared. Stepwise multiple linear regression models were generated using the most relevant variables identified by the above genetic algorithms. This procedure led to the successful generation of Quantitative Structure activity Relationship (QSAR) models for (a) proprietary datasets and (b) the Selwood dataset.
Related Concept Videos
Genetic Screens
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Evolutionary Relationships through Genome Comparisons
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Limits to Natural Selection
Mutation, Gene Flow, and Genetic Drift
Methods of Medium Optimization