C4.5: programs for machine learning
C4.5: programs for machine learning
Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Pattern recognition using boundary data of component distributions
Computers and Industrial Engineering
A novel data mining approach for the accurate prediction of translation initiation sites
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Prediction of translation initiation sites using classifier selection
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
StackTIS: A stacked generalization approach for effective prediction of translation initiation sites
Computers in Biology and Medicine
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Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.