NDLI: An integrated system for class prediction using gene expression profiling

Content Provider	IEEE Xplore Digital Library
Author	Dechang Chen Hua, D.D. Zhenqiu Liu Zhi-Fu Cheng
Copyright Year	2004
Description	Author affiliation: Preventive Medicine & Biometrics, Uniformed Services Univ. of the Health Sci., Bethesda, MD, USA (Dechang Chen)
Abstract	Gene expression profiles have been successfully applied to class prediction. Due to a large number of genes (features) and a small number of samples in gene expression data, feature selection is essential when performing the prediction task. Many methods have been proposed to select features in microarray data analysis, but there is no unique method which performs uniformly well for all the learning algorithms. It is then practical to find a feature selection method and a learning algorithm that give superior performance. In this paper, we present an integrated scheme to perform the task of class prediction based on gene expression profiles. The scheme incorporates a simple novel feature selection procedure into naive Bayes models. Each selected gene has a high score of discriminatory power determined by the Brown-Forsythe test statistics. Any pair of selected genes have a low correlation. This facilitates the use of the conditional independence among genes assumed by the naive Bayes models. To demonstrate the effectiveness, the proposed scheme was applied to three commonly used expression data sets COLON, OVARIAN, and LEUKEMIA. The results show that the numbers of misclassified samples are 0, and 4, respectively.
Starting Page	1023
Ending Page	1028
File Size	877144
Page Count	6
File Format	PDF
ISBN	0780386531
DOI	10.1109/ICARCV.2004.1468984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Filters Data analysis Testing Statistical analysis Biometrics Bridges Computer science Telemedicine Hospitals
Content Type	Text
Resource Type	Article

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