Hybrid approach based on principal component and multiple discriminant analysis for microarray classification
Authors: Al-Sultan, A. and Wani, M.A.
Journal: Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Pages: 954-960
ISBN: 9781601320551
Abstract:Cancer diagnosis is a major clinical applications area of gene expression microarray technology. The work presented in this paper describes a hybrid approach that employs principal component analysis (PCA) and multiple discriminant analysis (MDA) methods for microarray classification. The paper first describes a hybrid approach that incorporates PCA and linear discriminant analysis (LDA) for microarray classification. This hybrid approach effectively solves the singular scatter matrix problem caused by small training samples. The paper then explores a hybrid system that makes use of MDA instead of PCA for microarray classification. The resulting hybrid system increases the effective dimension of the projected subspace. A comprehensive evaluation of the hybrid systems was performed. The system was tested on a dataset of 62 samples (40 colon tumor and 22 normal colon tissue). The results show that the use of MDA increased the accuracy of classification of microarray data which will lead to better diagnosis of cancer and other diseases.
Source: Scopus
Preferred by: Mohammad Wani