The computational analysis of scientific literature to define and recognize gene expression clusters.

ABSTRACT: A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when defining expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional cohe

SUBMITTER: Raychaudhuri S 

PROVIDER: S-EPMC169898 | biostudies-literature | 2003 Aug

REPOSITORIES: biostudies-literature