Neural network with K-means clustering via PCA for gene expression profile analysis

Thomas C. Chen, Sandeep Sanga, Tina Y. Chou, Vittorio Cristini, Mary E. Edgerton

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

3 Scopus citations

Abstract

Gene expression microarray data are highly multidimensional and contain high level of noise. Most of these data involve multiple heterogeneous dynamic patterns depending on disease under study. In addition, possible errors might also be introduced along data collection path if multiple sites and methods are used. In this paper a combined data mining method, i.e., neural network with K-means clustering via principal component analysis (PCA), is proposed to address the data complexity issues when conducting gene expression profile mining. The proposed method was tested on gene expression profile in lung adenocarcinoma, collected from multiple cancer research centers, for survival prediction and risk assessment. The results from the proposed method were analyzed, and further studies for future improvement of the proposed method were also recommended

Original languageEnglish (US)
Title of host publication2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Pages670-673
Number of pages4
Volume3
DOIs
StatePublished - Nov 12 2009
Event2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, United States
Duration: Mar 31 2009Apr 2 2009

Other

Other2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
CountryUnited States
CityLos Angeles, CA
Period3/31/094/2/09

Keywords

  • Clustering analysis
  • Gene expression
  • K-mean
  • Lung cancer
  • Neural network
  • PCA

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Software

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