Title: Multi-model inference using mixed effects from a linear regression based genetic algorithm
Authors: Van der Borght, Koen ×
Verbeke, Geert
van Vlijmen, Herman #
Issue Date: 2014
Publisher: BioMed Central
Series Title: BMC Bioinformatics vol:15 pages:88
Article number: 10.1186/1471-2105-15-88
Abstract: Different high-dimensional regression methodologies exist for the selection of variables to predict a continuous variable. To improve the variable selection in case clustered observations are present in the training data, an extension towards mixed-effects modeling (MM) is requested, but may not always be straightforward to implement.In this article, we developed such a MM extension (GA-MM-MMI) for the automated variable selection by a linear regression based genetic algorithm (GA) using multi-model inference (MMI). We exemplify our approach by training a linear regression model for prediction of resistance to the integrase inhibitor Raltegravir (RAL) on a genotype-phenotype database, with many integrase mutations as candidate covariates. The genotype-phenotype pairs in this database were derived from a limited number of subjects, with presence of multiple data points from the same subject, and with an intra-class correlation of 0.92.
ISSN: 1471-2105
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
× corresponding author
# (joint) last author

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