Abstract
Predicting the secondary structure of a protein is a main topic in bioinformatics. A reliable predictor is needed by threading methods to improve the prediction of tertiary structure. Moreover, the predicted secondary structure content of a protein can be used to assign the protein to a specific folding class and thus estimate its function. We discuss here the use of support vector machines (SVMs) for the prediction of secondary structure. We show the results of a comparative experiment with a previously presented work. We measure the performances of SVMs on a significant non-redundant set of proteins. We present for the first time a direct comparison between SVMs and feed forward neural netwoks (NNs) for the task of secondary structure prediction. We exploit the use of bidirectional recurrent neural networks (BRNNs) as a filtering method to refine the predictions of the SVM classifier. Finally, we introduce a simple but effective idea to enforce constraints into secondary structure prediction based on finite-state automata (FSA) and Viterbi algorithm.
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Ceroni, A., Frasconi, P., Passerini, A., Vullo, A. (2003). A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_12
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DOI: https://doi.org/10.1007/978-3-540-39853-0_12
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