Meta-classification of Multi-type Cancer Gene Expression Data

Massive publicly available gene expression data consisting of different experimental conditions and microarray platforms introduce new challenges in data mining when integrating multiple gene expression data. In this work, we proposed a metaclassification algorithm, which is called MIF algorithm, to perform multi-type cancer gene expression data classification. It uses regular histograms for gene expression levels of certain significant genes to represent sample profiles. Differences between profiles are then used to obtain dissimilarity measures and indicators of predictive classes. In order to demonstrate the robustness of the algorithm, 10 different data sets, which are individually published in 8 publications, are experimented. The results show that the MIF algorithm outperforms the simple majority-voting meta-classification algorithm and has a good meta-classification performance. In addition, we also compare our results with other researchers' works, and the comparisons are...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Benny Y. M. Fung, Vincent T. Y. Ng
• Keywords: Data Mining, Gene Expression Data, Gene Expression Levels, KDD 2004, Keywords Gene Expression