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

Balaji Krishnapuram, Lawrence Carin, Alexander J. Hartemink

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

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 five 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 diagnosis. Focusing attention on a small subset of genes is useful not only because it produces a classifier with good generalization capacity, but also because this set of genes may provide insights into the mechanisms responsible for the disease itself. A number of the genes identified by the JCFO in our experiments are already in use as clinical markers for cancer diagnosis; some of the remaining genes may be excellent candidates for further clinical investigation. If it is possible to identify a small set of genes that is indeed capable of providing complete discrimination, inexpensive diagnostic assays might be widely deployable in clinical settings.
Original languageEnglish (US)
Title of host publicationJournal of Computational Biology
Pages227-242
Number of pages16
DOIs
StatePublished - Jul 22 2004
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Fingerprint Dive into the research topics of 'Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data'. Together they form a unique fingerprint.

Cite this