Finding signal peptides in human protein sequences using recurrent neural networks. 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).
References in zbMATH (referenced in 2 articles )
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- Choo, Khar Heng; Tan, Tin Wee; Ranganathan, Shoba: A comprehensive assessment of N-terminal signal peptides prediction methods (2009) ioport
- Reczko, Martin; Fiziev, Petko; Staub, Eike; Hatzigeorgiou, Artemis: Finding signal peptides in human protein sequences using recurrent neural networks (2002)