Title
Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier
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.
Year
DOI
Venue
2006
10.1016/j.neucom.2005.04.005
Neurocomputing
Keywords
Field
DocType
feature extraction,proposed classification scheme,gene expression data,pnn classifier,dwt feature extraction,tumor tissue identification,normal classification,neural network,gene expression,human lung carcinomas data,well-known data set,colon cancer data,discrete wavelet,lower dimensional feature space,discrete wavelet transform,feature extraction module,colon cancer,wavelet transform,feature space,probabilistic neural network
Feature vector,Data set,Pattern recognition,Computer science,Feature extraction,Probabilistic neural network,Discrete wavelet transform,Artificial intelligence,Classifier (linguistics),Artificial neural network,Machine learning,Wavelet transform
Journal
Volume
Issue
ISSN
69
4-6
Neurocomputing
Citations 
PageRank 
References 
15
0.81
14
Authors
3
Name
Order
Citations
PageRank
Guangmin Sun1213.40
Xiaoying Dong2274.32
Guandong Xu3150.81