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

Publication ,  Journal Article
Krishnapuram, B; Carin, L; Hartemink, AJ
Published in: Journal of computational biology : a journal of computational molecular cell biology
January 2004

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.

Duke Scholars

Published In

Journal of computational biology : a journal of computational molecular cell biology

DOI

EISSN

1557-8666

ISSN

1066-5277

Publication Date

January 2004

Volume

11

Issue

2-3

Start / End Page

227 / 242

Related Subject Headings

  • Regression Analysis
  • Neoplasms
  • Gene Expression Profiling
  • Data Interpretation, Statistical
  • Computational Biology
  • Bioinformatics
  • Bayes Theorem
  • Algorithms
  • 49 Mathematical sciences
  • 46 Information and computing sciences
 

Citation

APA
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ICMJE
MLA
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Krishnapuram, B., Carin, L., & Hartemink, A. J. (2004). Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology, 11(2–3), 227–242. https://doi.org/10.1089/1066527041410463
Krishnapuram, Balaji, Lawrence Carin, and Alexander J. Hartemink. “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.Journal of Computational Biology : A Journal of Computational Molecular Cell Biology 11, no. 2–3 (January 2004): 227–42. https://doi.org/10.1089/1066527041410463.
Krishnapuram B, Carin L, Hartemink AJ. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Journal of computational biology : a journal of computational molecular cell biology. 2004 Jan;11(2–3):227–42.
Krishnapuram, Balaji, et al. “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.Journal of Computational Biology : A Journal of Computational Molecular Cell Biology, vol. 11, no. 2–3, Jan. 2004, pp. 227–42. Epmc, doi:10.1089/1066527041410463.
Krishnapuram B, Carin L, Hartemink AJ. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Journal of computational biology : a journal of computational molecular cell biology. 2004 Jan;11(2–3):227–242.
Journal cover image

Published In

Journal of computational biology : a journal of computational molecular cell biology

DOI

EISSN

1557-8666

ISSN

1066-5277

Publication Date

January 2004

Volume

11

Issue

2-3

Start / End Page

227 / 242

Related Subject Headings

  • Regression Analysis
  • Neoplasms
  • Gene Expression Profiling
  • Data Interpretation, Statistical
  • Computational Biology
  • Bioinformatics
  • Bayes Theorem
  • Algorithms
  • 49 Mathematical sciences
  • 46 Information and computing sciences