Copyright © 2006 by Devika Subramanian

These materials are for educational use by members of the Rice Computer Science Department. Use for other purposes requires permission of the author.

Support Vector Machines and their applications

Support vector machines: the theory

• A Tutorial on Support Vector Machines for Pattern Recognition, C. J. C. Burges, Knowledge Discovery and Data Mining, 2(2), 1998.
• Statistical Learning and Kernel Methods, Bernhard Schölkopf, MSR-TR-2000-23, February 2000

SVMs for cancer diagnosis from gene expression data

• Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES, Science. 1999 Oct 15;286(5439):531-7.
• Knowledge-based analysis of microarray gene expression data by using support vector machines, Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terrence S. Furey, Manuel Ares, Jr., and David Haussler, PNAS 2000; 97: 262-267.
• Support vector machine classification and validation of cancer tissue samples using microarray expression data, Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D, Bioinformatics. 2000 Oct;16(10):906-14.
• Tissue classification with gene expression profiles, Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z, Comput Biol. 2000;7(3-4):559-83.
• Multiclass cancer diagnosis using tumor gene expression signatures, Sridhar Ramaswamy, Pablo Tamayo, Ryan Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, Christine Ladd, Michael Reich, Eva Latulippe, Jill P. Mesirov, Tomaso Poggio, William Gerald, Massimo Loda, Eric S. Lander, and Todd R. Golub, PNAS 2001 98: 15149-15154;
• Selection bias in gene extraction on the basis of microarray gene-expression data, Christophe Ambroise and Geoffrey J. McLachlan, PNAS 2002 99: 6562-6566.
• Classification of multiple cancer types by multicategory support vector machines using gene expression data, Lee Y, Lee CK, Bioinformatics. 2003 Jun 12;19(9):1132-9.

More SVM applications in computational biology

• Estimating dataset size requirements for classifying DNA microarray data, Mukherjee S, Tamayo P, Rogers S, Rifkin R, Engle A, Campbell C, Golub TR, Mesirov JP, J Comput Biol. 2003;10(2):119-42.
• Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data, Qian J, Lin J, Luscombe NM, Yu H, Gerstein M, Bioinformatics. 2003 Oct 12;19(15):1917-26.
• SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence, Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ, Nucleic Acids Res. 2003 Jul 1;31(13):3692-7.
• Classifying G-protein coupled receptors with support vector machine, Karchin R, Karplus K, Haussler D, Bioinformatics. 2002 Jan;18(1):147-59.
• Bio-support vector machines for computational proteomics, Yang ZR, Chou KC, Bioinformatics. 2004 Jan 29.

Applications in text mining

• Mapping subsets of scholarly information, Paul Ginsparg, Paul Houle, Thorsten Joachims, and Jae-Hoon Sul, PNAS published February 6, 2004,
• Mapping knowledge domains, Richard M. Shiffrin and Katy Börner, PNAS published January 23, 2004.

SVM Software

• mySVM, an SVM implementation for pattern recognition and regression.
• Spider, A library in MATLAB for classification, regression, clustering. For SVMs it uses LIBSVM and SVMLight.
• More SVM software