Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier

Authors:
Guangmin Sun;Xiaoying Dong;Guandong Xu
Affiliations:
Department of Electronic Engineering, Beijing University of Technology, No. 100, Ping Le Yuan, Chao Yang District, Beijing 100022, China;Department of Electronic Engineering, Beijing University of Technology, No. 100, Ping Le Yuan, Chao Yang District, Beijing 100022, China;Department of Etiology and Carcinogenesis, Peking Union Medical College, Beijing 100021, China
Venue:
Neurocomputing
Year:
2006

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Abstract

In this paper, we propose the joint use of discrete wavelet transform (DWT)-based feature extraction and probabilistic neural network (PNN) classifier to classify tissues using gene expression data. In the feature extraction module, gene expression data are firstly transformed into time-scale domain by DWT and then the reconstructed signals by using wavelet transform are reduced to a lower dimensional feature space. In the module of tissue classification, the outputs of the extractor are fed into a PNN classifier, and the class labels are given finally. Some test and comparison experiments have been made to evaluate the performance of the proposed classification scheme, using the features extracted with as well as without wavelet transform processing procedure. Correct rates of 92% and 98.7% in tumour vs. normal classification have been obtained using the proposed scheme on two well-known data sets: a colon cancer data set and a human lung carcinomas data set.