Knowledge-based analysis of microarray gene expression data by using support vector machines
The authors 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 flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. They test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, they use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.
- Publication Date:
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- DOE Contract Number:
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- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 18 Jun 2001
- Research Org:
- Department of Computer Science, UCSC (US)
- Sponsoring Org:
- US Department of Energy (US)
- Country of Publication:
- United States
- 59 BASIC BIOLOGICAL SCIENCES; COMPUTERS; DNA; FLEXIBILITY; FUNCTIONALS; GENES; HYBRIDIZATION; LEARNING; METRICS; VECTORS; YEASTS
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