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A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis

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 te...

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Detalles Bibliográficos
Autores principales: Wu, Wei, Liu, Xin, Xu, Min, Peng, Jin-Rong, Setiono, Rudy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5172540/
https://www.ncbi.nlm.nih.gov/pubmed/16393145
http://dx.doi.org/10.1016/S1672-0229(05)03013-5
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author Wu, Wei
Liu, Xin
Xu, Min
Peng, Jin-Rong
Setiono, Rudy
author_facet Wu, Wei
Liu, Xin
Xu, Min
Peng, Jin-Rong
Setiono, Rudy
author_sort Wu, Wei
collection PubMed
description 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.
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spelling pubmed-51725402016-12-23 A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis Wu, Wei Liu, Xin Xu, Min Peng, Jin-Rong Setiono, Rudy Genomics Proteomics Bioinformatics Article 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. Elsevier 2005 2016-11-28 /pmc/articles/PMC5172540/ /pubmed/16393145 http://dx.doi.org/10.1016/S1672-0229(05)03013-5 Text en . http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Wu, Wei
Liu, Xin
Xu, Min
Peng, Jin-Rong
Setiono, Rudy
A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title_full A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title_fullStr A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title_full_unstemmed A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title_short A Hybrid SOM-SVM Approach for the Zebrafish Gene Expression Analysis
title_sort hybrid som-svm approach for the zebrafish gene expression analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5172540/
https://www.ncbi.nlm.nih.gov/pubmed/16393145
http://dx.doi.org/10.1016/S1672-0229(05)03013-5
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