articleBioinformaticsApr 15, 2004BRONZE OA

A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

University of Rochester

PubMed
Indexed incrossrefdoajpubmed

Abstract

This paper studies the problem of building multiclass classifiers for tissue classification based on gene expression. The recent development of microarray technologies has enabled biologists to quantify gene expression of tens of thousands of genes in a single experiment. Biologists have begun collecting gene expression for a large number of samples. One of the urgent issues in the use of microarray data is to develop methods for characterizing samples based on their gene expression. The most basic step in the research direction is binary sample classification, which has been studied extensively over the past few years. This paper investigates the next step-multiclass classification of samples based on gene…

Citation impact

653
total citations
FWCI
12.65
Percentile
100%
References
47
Citations per year

Authors

3

Topics & keywords

Keywords
  • Feature selection
  • Multiclass classification
  • Computer science
  • Artificial intelligence
  • Expression (computer science)
  • Selection (genetic algorithm)
  • Sample (material)
  • Binary classification
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