摘要：Classification of cancers based on gene expressions produces better accuracy
when compared to that of the clinical markers. Feature selection improves
the accuracy of these classification algorithms by reducing the chance
of overfitting that happens due to large number of features. We develop a
new feature selection method called Biological Pathway-based Feature Selection (BPFS) for microarray data. Unlike most of the existing methods,
our method integrates signaling and gene regulatory pathways with gene
expression data to minimize the chance of overfitting of the method and to
improve the test accuracy. Thus, BPFS selects a biologically meaningful feature
set that is minimally redundant. Our experiments on published breast
cancer datasets demonstrate that all of the top 20 genes found by our method
are associated with cancer. Furthermore, the classification accuracy of our
signature is up to 18% better than that of vant Veers 70 gene signature,
and it is up to 8% better accuracy than the best published feature selection
method, I-RELIEF.
