SVM Classifier - A comprehensive java interface for support vector machine classification of microarray data

BMC Bioinformatics

Volumn 7, Issue SUPPL.4, 2006, Pages

  Pirooznia, Mehdi ^a   Deng, Youping ^a  

^a The University of Southern Mississippi   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; DNA MICRO-ARRAY; EXPRESSION DATA; GRAPHICAL APPLICATIONS; GRAPHICAL USER INTERFACES (GUI); GUI APPLICATIONS; SUPPORT VECTOR MACHINE CLASSIFICATION; SVM CLASSIFIERS;

GENE EXPRESSION; GRAPHICAL USER INTERFACES; JAVA PROGRAMMING LANGUAGE;

SUPPORT VECTOR MACHINES;

BRCA1 PROTEIN; BRCA2 PROTEIN; PROTEOME;

ACCURACY; ARTICLE; AUTOMATION; BREAST CANCER; CLASSIFICATION ALGORITHM; COMPUTER INTERFACE; DNA HYBRIDIZATION; DNA MICROARRAY; GENE FUNCTION; GENETIC ALGORITHM; MICROARRAY ANALYSIS; PREDICTION; RELIABILITY; SUPPORT VECTOR MACHINE; VALIDATION STUDY; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER LANGUAGE; EVALUATION STUDY; GENE EXPRESSION; GENE EXPRESSION PROFILING; METABOLISM; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION;

CLUSTER ANALYSIS; GENE EXPRESSION; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PROGRAMMING LANGUAGES; PROTEOME; SIGNAL TRANSDUCTION; USER-COMPUTER INTERFACE;

EID: 33947414058     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-S4-S25     Document Type: Article

References (11)

1. Noble WS: Support vector machine applications in computational biology. In Kernel Methods in Computational Biology Edited by: Schölkopf B, Tsuda K, Vert JP. MIT Press; 2004:71-92.
2. Vapnik VN: Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control Wiley: New York; 1998.
3. Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet C, Furey TS, Ares JM, Haussler D: Knowledge-based analysis of microarray gene expression data using support vector machines. Proc Natl Acad Sci USA 2000, 97:262-267.
4. Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Machine Learning 2001, 46(1-3):389-422.
5. Pavlidis P, Wapinski I, Noble WS: Support vector machine classification on the web. Bioinformatics 2004, 20(4):586-587.
6. Chih-Chung Chang, Chih-Jen Lin: LIBSVM, a library for support vector machines. 2001 [http://www.csie.ntu.edu.tw/~cjlin/libsvm].
7. Cortes C, Vapnik V: Support-vector network. Machine Learning 1995, 20:273-297.
8. Schölkopf B, Smola A, Williamson RC, Bartlett PL: New support vector algorithms. Neural Computation 2000, 12:1207-1245.
9. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC: Estimating the support of a high-dimensional distribution. Neural Computation 2001, 13(7):1443-1471.
10. Joachims T: Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning Edited by: Schölkopf B, Burges CJC, Smola AJ. Cambridge: MIT Press; 1998.
11. Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Raffeld M, et al.: Gene-expression profiles in hereditary breast cancer. N Engl J Med 2001, 344:539-548.