  Introduction: Feature selection and gene set analysis are of increasing
interest in bioinformatics. While these two approaches have been developed for
different purposes, we describe how some gene set analysis methods can be used
to conduct feature selection. Here we adapt the gene set analysis method,
significance analysis of microarray gene set reduction (SAMGSR), for feature
selection, and propose two extensions-simple SAMGSR and two-level SAMGSR to
identify relevant features for longitudinal microarray data.
  Results and Discussion: When applied to a real-world application, both simple
and two-level SAMGSR work comparably well. Using simulated data, we further
demonstrate that both SAMGSR extensions have the ability to identify the true
relevant genes. If the relevant genes are not highly correlated with the
irrelevant ones, the final models given by the two SAMGSR extensions are
parsimonious as well.
  Conclusions: By adapting SAMGSR for feature selection and applying the
proposed algorithms on a longitudinal gene expression dataset, we demonstrate
that a gene set analysis method can be used for the purpose of feature
selection. We believe this work paves the way for more research to bridge
feature selection and gene set analysis with the development of novel
algorithms.
