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Feature selection and gene set analysis are of increasinginterest in bioinformatics. While these two approaches have been developed fordifferent purposes, we describe how some gene set analysis methods can be usedto conduct feature selection. Here we adapt the gene set analysis method,significance analysis of microarray gene set reduction (SAMGSR), for featureselection, and propose two extensions-simple SAMGSR and two-level SAMGSR toidentify relevant features for longitudinal microarray data.  Results and Discussion: When applied to a real-world application, both simpleand two-level SAMGSR work comparably well. Using simulated data, we furtherdemonstrate that both SAMGSR extensions have the ability to identify the truerelevant genes. If the relevant genes are not highly correlated with theirrelevant ones, the final models given by the two SAMGSR extensions areparsimonious as well.  Conclusions: By adapting SAMGSR for feature selection and applying theproposed algorithms on a longitudinal gene expression dataset, we demonstratethat a gene set analysis method can be used for the purpose of featureselection. We believe this work paves the way for more research to bridgefeature selection and gene set analysis with the development of novelalgorithms.

Feature selection for longitudinal microarray data by adapting a pathway analysis method