CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Transmembrane segments prediction and understanding using support vector machine and decision tree
Expert Systems with Applications: An International Journal
International Journal of Data Mining and Bioinformatics
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We modified an existing association rule based classifier CPAR to improve traditional black box model based learning machine approaches on Transmembrane (TM) segment prediction. The modified classifier was improved further by combining with SVM. The experimental results indicate that this hybrid scheme offers biologically meaningful rules on TM/EM segment prediction while maintaining the performance almost as well as the SVM method. The evaluation of the sturdiness and the Receiver Operating Characteristic (ROC) curve analysis proved that this new scheme is robust and competent with SVM on TM/EM segment prediction. The prediction server is available at http:/ /bmcc2.cs.gsu.edu/∼haeh2/.