Discretization provides a conceptually simple tool to build expression networks.

ABSTRACT: Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynT

SUBMITTER: Vass JK 

PROVIDER: S-EPMC3078920 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature