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Bioinformatics Vol. 16 no. 3 2000
Pages 245-250
© 2000 Oxford University Press

Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites

B. Jagla 1 and J. Schuchhardt 2,*

1 Freie Universität Berlin, Institut für Med./Techn. Physik und Lasermedizin, Director Prof. Dr. Ing. G. Müller, Prof. h.c. Dr. h.c., Krahmerstrasse 6-10, 12045 Berlin, Germany
2 Humboldt Universität zu Berlin, Innovationskolleg Theoretische Biologie, Invalidenstraße 43, 10115 Berlin, Germany

Received on September 1, 1999 ; revised on October 29, 1999 ; accepted on December 13, 1999

Motivation: Data representation and encoding are essential for classification of protein sequences with artificial neural networks (ANN). Biophysical properties are appropriate for low dimensional encoding of protein sequence data. However, in general there is no a priori knowledge of the relevant properties for extraction of representative features.

Results: An adaptive encoding artificial neural network (ACN) for recognition of sequence patterns is described. In this approach parameters for sequence encoding are optimized within the same process as the weight vectors by an evolutionary algorithm. The method is applied to the prediction of signal peptide cleavage sites in human secretory proteins and compared with an established predictor for signal peptides.

Conclusion: Knowledge of physico-chemical properties is not necessary for training an ACN. The advantage is a low dimensional data representation leading to computational efficiency, easy evaluation of the detected features, and high prediction accuracy.

Availability: A cleavage site prediction server is located at the Humboldt University http://itb.biologie.hu-berlin.de/~jo/sig-cleave/ACNpredictor.cgi

Contact: jo{at}itb.hu-berlin.de; berndj{at}zedat.fu-berlin.de

*To whom correspondence should be addressed.


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