Multi-scale feature extraction and classification of gene expression data

doi:10.3778/j.issn.1002-8331.2009.13.042

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (13): 144-145.DOI: 10.3778/j.issn.1002-8331.2009.13.042

• 数据库、信号与信息处理 • Previous Articles Next Articles

Multi-scale feature extraction and classification of gene expression data

WU Ya-zhou,ZHANG Ling,LUO Wan-chun,YI Dong

Department of Health Statistics，Third Military Medical University，Chongqing 400038，China

Received:2008-11-19 Revised:2009-01-22 Online:2009-05-01 Published:2009-05-01
Contact: WU Ya-zhou

基因表达数据的多尺度特征提取与分类研究

伍亚舟,张玲,罗万春,易东

第三军医大学卫生统计学教研室，重庆 400038

通讯作者: 伍亚舟

Abstract

Abstract: To search a new and effective method for feature extraction and classification based on microarray expression data.The features of gene expression are extracted by the wavelet multi-resolution analysis，the features are classified by the support vector machines and BP neural network methods.There are multi-scale feature gene expression，the maximum classification rate is 98.61％，with stable results.Multi-scale theory analysis of gene expression is a new and effective bioinformatics method，which is worth further exploration and research.

Key words: microarray, gene expression data, multi-resolution analysis（MRA）, Support Vector Machines（SVM）

摘要： 基于微阵列表达数据，探索新的有效特征提取和分类方法。采用小波多分辩率分析方法提取基因表达的特征，利用支持向量机和BP神经网络方法进行分类。基因表达具有明显的多尺度特征，分类率最大达到98.61％，结果稳定。采用多尺度理论对基因表达数据进行分析是一种新的有效的生物信息学方法，值得进一步探索与研究。

关键词: 微阵列, 基因表达数据, 多分辨率分析, 支持向量机

WU Ya-zhou,ZHANG Ling,LUO Wan-chun,YI Dong. Multi-scale feature extraction and classification of gene expression data[J]. Computer Engineering and Applications, 2009, 45(13): 144-145.

伍亚舟,张玲,罗万春,易东. 基因表达数据的多尺度特征提取与分类研究[J]. 计算机工程与应用, 2009, 45(13): 144-145.

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Multi-scale feature extraction and classification of gene expression data

基因表达数据的多尺度特征提取与分类研究

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