Classification of normal and tumor tissues using geometric representation of gene expression microarray data

Saejoon Kim, Donghyuk Shin

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

Abstract

Microarray is a fascinating technology that provides us with accurate predictions of the state of biological tissue samples simply based on the expression levels of genes available from it. Of particular interest in the use of microarray technology is the classification of normal and tumor tissues which is vital for accurate diagnosis of the disease of interest. In this paper, we shall make use of geometric representation from graph theory for the classification of normal and tumor tissues of colon and ovary. The accuracy of our geometric representation-based classification algorithm will be shown to be comparable to that of the currently known best classification algorithms for the two datasets. In particular, the presented algorithm will be shown to have the highest classification accuracy when the number of genes used for classification is small.

Original languageEnglish (US)
Title of host publicationModeling Decisions for Artificial Intelligence - 4th International Conference, MDAI 2007, Proceedings
PublisherSpringer Verlag
Pages393-402
Number of pages10
ISBN (Print)9783540737285
DOIs
StatePublished - 2007
Externally publishedYes
Event4th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2007 - Kitakyushu, Japan
Duration: Aug 16 2007Aug 18 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4617 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2007
CountryJapan
CityKitakyushu
Period8/16/078/18/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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