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RECOMB
2003
Springer

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

14 years 10 months ago
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...
Balaji Krishnapuram, Lawrence Carin, Alexander J.
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2003
Where RECOMB
Authors Balaji Krishnapuram, Lawrence Carin, Alexander J. Hartemink
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