M@CBETH: a microarray classification benchmarking tool

  • Authors:

  • Nathalie L. M. M. Pochet;Frizo A. L. Janssens;Frank De Smet;Kathleen Marchal;Johan A. K. Suykens;Bart L. R. De Moor

  • Affiliations:

  • K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium;K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium;K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium;K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium;K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium;K. U. Leuven, ESAT--SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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Abstract

Summary: Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification. Availability: Web service at http://www.esat.kuleuven.be/MACBETH/ Contact: Nathalie.Pochet@esat.kuleuven.be