title: A hybrid SOM-SVM approach for the zebrafish gene expression analysis.
abstract: Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map (SOM) and the support vector machine (SVM) for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.
authors.affiliation: Laboratory of Functional Genomics, Institute of Molecular and Cell Biology, Singapore.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Min
authors.initials: M
authors.lastname: Xu
authors.forename: Jin Rong
authors.initials: JR
authors.lastname: Peng
authors.forename: Rudy
authors.initials: R
authors.lastname: Setiono
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D005810:Multigene Family / Q000235:genetics; D015027:Zebrafish / Q000145:classification / Q000235:genetics*