New feature selection algorithms for linear threshold functions are described which combine backward elimination with an adaptive regularization method. This makes them particular...
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
This paper introduces the first integrated algorithm designed to discover novel motifs in heterogeneous sequence data, which is comprised of coregulated genes from a single genome...
We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signat...
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may ...