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The Strength of Weak Learnability
Machine Learning
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Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
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ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
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Expert Systems with Applications: An International Journal
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ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is needed for large-scale genome analysis. In this paper, we introduce a novel subcellular prediction method combining boosting algorithm with probabilistic neural network algorithm. This new approach provided superior prediction performance compared with existing methods. The total prediction accuracy on Reinhardt and Hubbard's dataset reached up to 92.8% for prokaryotic protein sequences and 81.4% for eukaryotic protein sequences under 5-fold cross validation. On our new dataset, the total accuracy achieved 83.2%. This novel method provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementing method to other existing methods based on sorting singals.