title: Systems Approach to Identifying Relevant Pathways from Phenotype Information in Dose-Dependent Time Series Microarray Data.
abstract: This study presents a novel computational approach to find relevant pathways from dose-dependent time series gene expression data which are significantly associated with a phenotype pattern pathological patterns in the comprehensive evaluation of database of pathways. Our system uses four steps: 1) identify a set of genes which change significantly in dose or time; 2) find phenotype patterns and gene coefficients for the genes found in step 1; 3) expand to genome-wide coefficients, and 4) identify pathways which are significantly relevant to a phenotype pattern. Our technique finds biologically relevant pathways with and without phenotype-constraints. Our system has been used on genome-wide expression profiles of mouse lungs (n=160) following aspiration of well dispersed multi-walled carbon nanotubes (MWCNT), in order to detect MWCNT-induced lung inflammation and related pathways. The identified significant pathways are supported by evidence in the literature and biological validation.
authors.affiliation: Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506, USA.
authors.forename: Julian
authors.initials: J
authors.lastname: Dymacek
authors.affiliation: Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506, USA.
authors.forename: Nancy Lan
authors.initials: NL
authors.lastname: Guo
keywords: dose-dependent time series microarray data; nanoparticles; pathways; toxicogenomics