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Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines (2000)
| Citations: | 520 - 8 self |
BibTeX
@MISC{Brown00knowledge-basedanalysis,
author = {Michael P. S. Brown and William Noble Grundy and David Lin and Nello Cristianini and Charles Walsh Sugnet and Terrence S. Furey and Manuel Ares and Jr. and David Haussler},
title = {Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines},
year = {2000}
}
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Abstract
We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their exibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability t...
Keyphrases
support vector machine knowledge-based analysis microarray gene expression data gene function self-organizing map many mathematical feature gene expression data dna microarray hybridization experiment large data set hierarchical clustering expression data prior knowledge similarity function similar function supervised computer svms avoid several problem gene expression analysis unknown gene
