A probabilistic neural network for gene selection and classification of microarray data

Daniel Berrar, C. Stephen Downes, Werner Dubitzky

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

Abstract

In this paper, we present the mathematical foundations of a probabilistic neural network for gene selection and classification of high-dimensional microarray data. We present a catalogue of features that a classification system for microarray data should incorporate. We then use this catalogue and compare the theoretical properties of probabilistic neural networks with support vector machines with regard to their suitability for multiclass cancer prediction. We compare the classification performance of a probabilistic neural network with the performance of a support vector machine on a multiclass microarray data set. The results of the theoretical and practical comparison suggest that the probabilistic neural network approach is to be preferred over support vector machines for multiclass cancer classification using microarray data.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Artificial Intelligence IC-AI 2003
EditorsH.R. Arabnia, R. Joshua, Y. Mun
PublisherCSREA Press
Pages342-349
Number of pages8
ISBN (Print)1932415122, 9781932415124
Publication statusPublished - 2003 Jan 1
EventProceedings of the International Conference on Artificial Intelligence, IC-AI 2003 - Las Vegas, NV, United States
Duration: 2003 Jun 232003 Jun 26

Publication series

NameProceedings of the International Conference on Artificial Intelligence IC-AI 2003
Volume1

Other

OtherProceedings of the International Conference on Artificial Intelligence, IC-AI 2003
Country/TerritoryUnited States
CityLas Vegas, NV
Period03/6/2303/6/26

Keywords

  • Cancer classification
  • Microarray
  • Probabilistic neural network
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence

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