Joint classifier and feature optimization for cancer diagnosis using gene expression data

Recent research has demonstrated quite convincingly that accurate cancer diagnosis can be achieved by constructing classifiers that are designed to compare the gene expression profile of a tissue of unknown cancer status to a database of stored expression profiles from tissues of known cancer status. This paper introduces the JCFO, a novel algorithm that uses a sparse Bayesian approach to jointly identify both the optimal nonlinear classifier for diagnosis and the optimal set of genes on which to base that diagnosis. We show that the diagnostic classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods in a full leave-one-out cross-validation study of two widely used benchmark datasets. In addition to its superior classification accuracy, the algorithm is designed to automatically identify a small subset of genes (typically around twenty in our experiments) that are capable of providing complete discriminatory information for diagnosi...

• Type: Conference
• Year: 2003
• Where: RECOMB
• Authors: Balaji Krishnapuram, Lawrence Carin, Alexander J. Hartemink
• Keywords: Computational Biology, Leave-one-out Cross-validation Study, Recognition--Design Methodology Permission, RECOMB 2003, Sparse Bayesian Approach