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

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.