Tumor classification based on gene microarray data and hybrid learning method

被引：10

作者：

Liu, J ^{[1
]}

Zhou, HB ^{[1
]}

机构：

[1] Wuhan Univ, Dept Comp Sci, Wuhan 430072, HUbei, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

tumor classification; pareto optimization; MOEA;

D O I：

10.1109/ICMLC.2003.1259886

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gene expression microarray data can be used to classify tumor types. We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA/WV (Multi-Objective Evolutionary Algorithm/ Weighted Voting). MOEA is used to search for a relatively few subsets of informative genes from the high-dimensional gene space, and WV is used as a classification tool. This new method has been applied to predicate the subtypes of lymphoma and outcomes of medulloblastoma. The results are relatively accurate and meaningful compared with those from other methods.

引用

页码：2275 / 2280

页数：6

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