PUBLICATION

A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Authors
Wu, W., Liu, X., Xu, M., Peng, J.R., and Setiono, R.
ID
ZDB-PUB-060113-3
Date
2005
Source
Genomics, proteomics & bioinformatics   3(2): 84-93 (Journal)
Registered Authors
Peng, Jinrong
Keywords
self-organizing map, support vector machine, clustering, classification
MeSH Terms
  • Animals
  • Computational Biology/methods*
  • Gene Expression Profiling/methods*
  • Gene Expression Regulation/genetics*
  • Multigene Family/genetics
  • Zebrafish/classification
  • Zebrafish/genetics*
PubMed
16393145 Full text @ Genomics Proteomics Bioinformatics
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.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping