  Introduction
  It has been demonstrated that a pathway-based feature selection method which
incorporates biological information within pathways into the process of feature
selection usually outperform a gene-based feature selection algorithm in terms
of predictive accuracy, stability, and biological interpretation. Significance
analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a
gene set analysis method with further reduction of the selected pathways to
their respective core subsets, can be regarded as a pathway-based feature
selection method.
  Results and Discussion
  In SAMGSR, whether a gene is selected is mainly determined by its expression
difference between the phenotypes, and partially by the number of pathways to
which this gene belongs, but ignoring the topology information among pathways.
In this study, we propose a weighted version of the SAMGSR algorithm by
constructing weights based on the connectivity among genes and then
incorporating these weights in the test statistic.
  Conclusions
  Using both simulated and real-world data, we evaluate the performance of the
proposed SAMGSR extension and demonstrate that gene connectivity is indeed
informative for feature selection.
