Abstract
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 cross-validation). The Sigfind system is available on the WWW for predictions (http://www.stepc.gr/ synaptic/sig.nd.html).
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Reczko, M., Fiziev, P., Staub, E., Hatzigeorgiou, A. (2002). Finding Signal Peptides in Human Protein Sequences Using Recurrent Neural Networks. In: Guigó, R., Gusfield, D. (eds) Algorithms in Bioinformatics. WABI 2002. Lecture Notes in Computer Science, vol 2452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45784-4_5
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DOI: https://doi.org/10.1007/3-540-45784-4_5
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