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

Wenmin Liu; Zhen Ji; Shan He; Zexuan Zhu

doi:10.1109/CEC.2012.6256144

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs	https://doi.org/10.1109/CEC.2012.6256144
Publication status	Published - 2012
Event	2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012

Publication series

Name	2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference	2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	10/06/12 → 15/06/12

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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10.1109/CEC.2012.6256144

Cite this

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title = "Survival analysis of gene expression data using PSO based radial basis function networks",

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.",

author = "Wenmin Liu and Zhen Ji and Shan He and Zexuan Zhu",

year = "2012",

doi = "10.1109/CEC.2012.6256144",

language = "English",

isbn = "9781467315098",

series = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

booktitle = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

}

Liu, W, Ji, Z, He, S & Zhu, Z 2012, Survival analysis of gene expression data using PSO based radial basis function networks. in 2012 IEEE Congress on Evolutionary Computation, CEC 2012., 6256144, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, Brisbane, QLD, Australia, 10/06/12. https://doi.org/10.1109/CEC.2012.6256144

TY - GEN

T1 - Survival analysis of gene expression data using PSO based radial basis function networks

AU - Liu, Wenmin

AU - Ji, Zhen

AU - He, Shan

AU - Zhu, Zexuan

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866880825&partnerID=8YFLogxK

U2 - 10.1109/CEC.2012.6256144

DO - 10.1109/CEC.2012.6256144

M3 - Conference contribution

AN - SCOPUS:84866880825

SN - 9781467315098

T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

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

Abstract

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