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Constructing the gene regulation-level representation of microarray data for cancer classification

构造基因为癌症的微数组数据的规定级的表示分类

作     者:Wong, Hau-San Wang, Hong-Qiang 

作者机构:City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China 

出 版 物:《JOURNAL OF BIOMEDICAL INFORMATICS》 (生物医学情报学杂志)

年 卷 期:2008年第41卷第1期

页      面:95-105页

核心收录:

学科分类:1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术(可授工学、理学学位)] 10[医学] 

基  金:Research Grants Council of Hong Kong Special Administrative Region  China 

主  题:cancer classification genetic algorithms gene expression levels gene regulation levels histogram microarray data 

摘      要:In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for cancer classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for cancer classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the cancer classification capability but also facilitates the visualization of microarray data. (C) 2007 Elsevier Inc. All rights reserved.

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