Using prior knowledge and rule induction methods to discover molecular markers of prognosis in lung cancer.
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.
Frey
Lewis
L
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
Edgerton
Mary E
ME
Fisher
Douglas H
DH
Tang
Lianhong
L
Chen
Zhihua
Z
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
D014408:Biomarkers, Tumor