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 to those from other methods. Key words bioinformatics - tumor classification - Pareto optimization - MOEA CLC number Q 786 - TP 181 Foundation item: Supported by the National Natural Science Foundation of China (60301009), the Foundation of Young Scholars of Ministry of Education of China (150118) and Chenguang Project of Wuhan City (211121009).Biography: Liu Juan (1970-), female, Associate Professor, Postdoctoral, research direction: bioinformatics, data mining, machine learning.
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 to those from other methods. Key words bioinformatics - tumor classification - Pareto optimization - MOEA CLC number Q 786 - TP 181 Foundation item: Supported by the National Natural Science Foundation of China (60301009), the Foundation of Young Scholars of Ministry of Education of China (150118) and Chenguang Project of Wuhan City (211121009).Biography: Liu Juan (1970-), female, Associate Professor, Postdoctoral, research direction: bioinformatics, data mining, machine learning.
作者
Liu Juan 1,2, Hitoshi Iba 3 1. School of Computer, Wuhan University, Wuhan 430072, Hubei, China
2. State Key Laboratory of Software Engineering, Wuhan 430072, Hubei, China
3. Department of Frontier Informatics, University of Tokyo, Tokyo 113-8656, Japan
基金
SupportedbytheNationalNaturalScienceFoundationofChina (60 30 1 0 0 9),theFoundationofYoungScholarsofMinistryofEducationofChina (1 50 1 18)andChenguangProjectofWuhanCity(2 1 1 1 2 1 0 0 9)
