Prediction of tumor outcome based on gene expression data

  • Liu Juan
  • Hitoshi Iba


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 


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Copyright information

© Springer 2004

Authors and Affiliations

  • Liu Juan
    • 1
    • 2
  • Hitoshi Iba
    • 3
  1. 1.School of ComputerWuhan UniversityWuhan HubeiChina
  2. 2.State Key Laboratory of Software EngineeringWuhan HubeiChina
  3. 3.Department of Frontier InformaticsUniversity of TokyoTokyoJapan

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