Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Prediction of Signal Peptides and Signal Anchors by a Hidden Markov Model
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Bidirectional Dynamics for Protein Secondary Structure Prediction
Sequence Learning - Paradigms, Algorithms, and Applications
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian sequence learning for predicting protein cleavage points
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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A new approach called Sigfind for the prediction of signal peptides in human protein sequences is introduced. The method is based on the bidirectional recurrent neural network architecture. The modifications to this architecture and a better learning algorithm result in a very accurate identification of signal peptides (99.5% correct in fivefold crossvalidation). The Sigfind system is available on the WWW for predictions (http://www.stepc.gr/synaptic/sigfind.html).