Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Computational Applications of DNA Structural Scales
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Improving Promoter Prediction Using Multiple Instance Learning
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physico-chemical and sequence based techniques.