title: Using prior knowledge and rule induction methods to discover molecular markers of prognosis in lung cancer.
abstract: An iterative computational scientific discovery approach is proposed and applied to gene expression data for resectable lung adenocarcinoma patients. We use genes learned from the C5.0 rule induction algorithm, clinical features and prior knowledge derived from a network of interacting genes as represented in a database obtained with PathwayAssist to discover markers for prognosis in the gene expression data. This is done in an iterative fashion with machine learning techniques seeding the prior knowledge. This research illustrates the utility of combining signaling networks and machine learning techniques to produce simple prognostic classifiers.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
authors.forename: Lewis
authors.initials: L
authors.lastname: Frey
authors.forename: Mary E
authors.initials: ME
authors.lastname: Edgerton
authors.forename: Douglas H
authors.initials: DH
authors.lastname: Fisher
authors.forename: Lianhong
authors.initials: L
authors.lastname: Tang
authors.forename: Zhihua
authors.initials: Z
authors.lastname: Chen
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics* / Q000401:mortality; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality; D011379:Prognosis; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor