A novel dimensionality reduction technique based on independent component analysis for modeling microarray gene expression data

Han Liu, Rafal Kustra, Ji Zhang

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

Abstract

DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the "curse of dimensionality problem". An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for dimensionality reduction of gene expression data which is optimal in the sense of least square error. In this paper we propose a new dimensionality reduction technique for specific bioinformatics applications based on Independent component Analysis(ICA). Being able to exploit higher order statistics to identify a linear model result, this ICA based dimensionality reduction technique outperforms PCA from both statistical and biological significance aspects. We present experiments on NCI 60 dataset to show this result.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)
EditorsH.R. Arabnia, M. Youngsong
Pages1133-1139
Number of pages7
StatePublished - Dec 1 2004
EventProceedings of the International Conference on Artificial Intelligence, IC-AI'04 - Las Vegas, NV, United States
Duration: Jun 21 2004Jun 24 2004

Publication series

NameProceedings of the International Conference on Artificial Intelligence, IC-AI'04
Volume2

Other

OtherProceedings of the International Conference on Artificial Intelligence, IC-AI'04
CountryUnited States
CityLas Vegas, NV
Period6/21/046/24/04

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Keywords

  • Dimensionality reduction
  • Gene expression data
  • Independent component analysis
  • Latent regulatory factors

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  • Cite this

    Liu, H., Kustra, R., & Zhang, J. (2004). A novel dimensionality reduction technique based on independent component analysis for modeling microarray gene expression data. In H. R. Arabnia, & M. Youngsong (Eds.), Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04) (pp. 1133-1139). (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04; Vol. 2).