2000
DOI: 10.1073/pnas.97.1.262
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Knowledge-based analysis of microarray gene expression data by using support vector machines

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… Show more

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Cited by 1,967 publications
(1,058 citation statements)
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References 25 publications
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“…Finally we considered a supervised learning algorithm, i.e. linear (SVM-l ) and Gaussian (SVM-g) Support Vector Machines (SVMs), since it has been shown that SVMs are among the best supervised algorithms for predicting gene function (Brown et al, 2000;Pavlidis et al, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…Finally we considered a supervised learning algorithm, i.e. linear (SVM-l ) and Gaussian (SVM-g) Support Vector Machines (SVMs), since it has been shown that SVMs are among the best supervised algorithms for predicting gene function (Brown et al, 2000;Pavlidis et al, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…Support vector machines (SVMs) techniques allow the use of training sets to uncover patterns that discriminate between classes. SVMs have been shown to perform well in multiple areas of biological analysis including evaluating microarray date (Brown et al, 2000). We attempted to use this algorithm to predict the classification of samples into cancer and non-cancer groups.…”
Section: Microarray Construction and Preparationmentioning
confidence: 99%
“…Prophet uses a cross-validation schema specially designed to produce unbiased cross-validation errors, and implements different state-of-the-art methods for gene selection and prediction. In the current study, the Support Vector Machines (SVM) algorithm (Brown et al, 2000) with the radial basis function kernel method was used for classification rule generation, and the F-ratio gene filter approach for gene selection. All available tumors were included.…”
Section: Functional Profiling Of Dedifferentiation and Aggressivenessmentioning
confidence: 99%