A filter-based feature selection approach for identifying potential biomarkers for lung cancer.

ABSTRACT:

Background

Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identi

SUBMITTER: Lee IH 

PROVIDER: S-EPMC3164604 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature