Survival analysis of gene expression data using PSO based radial basis function networks

Wenmin Liu, Zhen Ji, Shan He, Zexuan Zhu*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Gene expression data combined with clinical data has emerged as an important source for survival analysis. However, gene expression data is characterized with thousands of features/genes but only tens or hundreds of observations. The high-dimensionality and unbalance between features and samples pose big challenges for the classical survival analysis methods. This paper proposes a particle swarm optimization based radial basis function networks (PSO-RBFN) for the survival analysis on gene expression data. Particularly, PSO-RBFN applies a principle component analysis for dimensionality reduction and optimizes the RBF network using PSO. The experimental results on three gene expression datasets indicate that PSO-RBFN is able to improve the predict accuracy compared to the other classical survival analysis methods.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Bibliographical note

Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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