C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Characterization of Prokaryotic and Eukaryotic Promoters Using Hidden Markov Models
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Consistency-based search in feature selection
Artificial Intelligence
Promoter Recognition for E. coli DNA Segments by Independent Component Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The accuracy of promoter recognition depends upon not only the appropriate representation of the promoter sequence but also the essential features of the sequence. These two important issues are addressed in this paper. Firstly, a promoter sequence is captured in form of a Chaos Game Representation (CGR). Then, based on the concept of Mahalanobis distance, a new statistical feature extraction is introduced to select a set of the most significant pixels from the CGR. The recognition is performed by a supervised neural network. This proposed technique achieved 100% accuracy when it is tested with the E.coli promoter sequences using a leave-one-out method. Our approach also outperforms other techniques.