Knowledge-based analysis of microarray gene expression data by using support vector machines. Export

@article{citeulike:1001233, 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 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. We 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, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.}, author = {Brown, M. P. and Grundy, W. N. and Lin, D. and Cristianini, N. and Sugnet, C. W. and Furey, T. S. and Ares, M. and Haussler, D.}, citeulike-article-id = {1001233}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.97.1.262}, citeulike-linkout-1 = {http://www.pnas.org/content/97/1/262.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/97/1/262.full.pdf}, citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/97/1/262}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/10618406}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=10618406}, day = {4}, doi = {10.1073/pnas.97.1.262}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {microarray, pnas, svm}, month = {January}, number = {1}, pages = {262--267}, posted-at = {2009-11-08 00:39:10}, priority = {2}, title = {Knowledge-based analysis of microarray gene expression data by using support vector machines.}, url = {http://dx.doi.org/10.1073/pnas.97.1.262}, volume = {97}, year = {2000} }

TY - JOUR ID - citeulike:1001233 L3 - citeulike-article-id:1001233 N2 - 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 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. We 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, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data. IS - 1 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 EP - 267 TI - Knowledge-based analysis of microarray gene expression data by using support vector machines. VL - 97 SP - 262 KW - microarray KW - pnas KW - svm AU - Brown, MP AU - Grundy, WN AU - Lin, D AU - Cristianini, N AU - Sugnet, CW AU - Furey, TS AU - Ares, M AU - Haussler, D PY - 2000/01/04/ UR - http://dx.doi.org/10.1073/pnas.97.1.262 ER -