title: Identification of functional modules that correlate with phenotypic difference: the influence of network topology.
abstract: One of the important challenges to post-genomic biology is relating observed phenotypic alterations to the underlying collective alterations in genes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis.
authors.affiliation: Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02215, USA.
authors.forename: Jui-Hung
authors.initials: JH
authors.lastname: Hung
authors.forename: Troy W
authors.initials: TW
authors.lastname: Whitfield
authors.forename: Tun-Hsiang
authors.initials: TH
authors.lastname: Yang
authors.forename: Zhenjun
authors.initials: Z
authors.lastname: Hu
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Weng
authors.forename: Charles
authors.initials: C
authors.lastname: DeLisi
mesh_terms: D000236:Adenoma / Q000235:genetics; D015179:Colorectal Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D005838:Genotype; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D010641:Phenotype; D055752:Small Cell Lung Carcinoma / Q000235:genetics