SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.
BACKGROUND
Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction.


RESULTS
The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM.


CONCLUSIONS
We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.
Pirooznia
Mehdi
M
Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, Mississippi 39406, USA. mehdi.pirooznia@usm.edu
Deng
Youping
Y
D016000:Cluster Analysis; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011381:Programming Languages; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014584:User-Computer Interface
D020543:Proteome