Identification of functional modules that correlate with phenotypic difference: the influence of network topology.
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.
Hung
Jui-Hung
JH
Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02215, USA.
Whitfield
Troy W
TW
Yang
Tun-Hsiang
TH
Hu
Zhenjun
Z
Weng
Zhiping
Z
DeLisi
Charles
C
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