Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
2005 Special Issue: Learning protein secondary structure from sequential and relational data
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Prediction of the disulphide bridges in proteins using SVM
International Journal of Bioinformatics Research and Applications
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
WSEAS Transactions on Computers
An overview of AI research in Italy
Artificial intelligence
Predicting Metal-Binding Sites from Protein Sequence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Information Sciences: an International Journal
Computers in Biology and Medicine
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Motivation: We focus on the prediction of disulfide bridges in proteins starting from their amino acid sequence and from the knowledge of the disulfide bonding state of each cysteine. The location of disulfide bridges is a structural feature that conveys important information about the protein main chain conformation and can therefore help towards the solution of the folding problem. Existing approaches based on weighted graph matching algorithms do not take advantage of evolutionary information. Recursive neural networks (RNN), on the other hand, can handle in a natural way complex data structures such as graphs whose vertices are labeled by real vectors, allowing us to incorporate multiple alignment profiles in the graphical representation of disulfide connectivity patterns. Results: The core of the method is the use of machine learning tools to rank alternative disulfide connectivity patterns. We develop an ad-hoc RNN architecture for scoring labeled undirected graphs that represent connectivity patterns. In order to compare our algorithm with previous methods, we report experimental results on the SWISS-PROT 39 dataset. We find that using multiple alignment profiles allows us to obtain significant prediction accuracy improvements, clearly demonstrating the important role played by evolutionary information. Availability: The Web interface of the predictor is available at http://neural.dsi.unifi.it/cysteines