C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid learning using genetic algorithms and decision trees for pattern classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A Clustering Based Hybrid System for Mass Spectrometry Data Analysis
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Information Sciences: an International Journal
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One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches-genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)-are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.