Please use this identifier to cite or link to this item: https://doi.org/10.1115/1.1857918
Title: A hybrid SOM-SVM approach for the zebrafish gene expression analysis
Authors: Wu, W.
Liu, X.
Xu, M.
Peng, J.-R.
Setiono, R. 
Keywords: Classification
Clustering
Self-organizing map
Support vector machine
Issue Date: 2005
Source: Wu, W., Liu, X., Xu, M., Peng, J.-R., Setiono, R. (2005). A hybrid SOM-SVM approach for the zebrafish gene expression analysis. Genomics, Proteomics and Bioinformatics 3 (2) : 84-93. ScholarBank@NUS Repository. https://doi.org/10.1115/1.1857918
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.
Source Title: Genomics, Proteomics and Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/42521
ISSN: 16720229
DOI: 10.1115/1.1857918
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