Prediction of subcellular localization using sequence-biased recurrent networks

Bioinformatics

Volumn 21, Issue 10, 2005, Pages 2279-2286

  Bodén, Mikael ^a   Hawkins, John ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

PEPTIDE;

ACCURACY; AMINO ACID SEQUENCE; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELLULAR DISTRIBUTION; CONTROLLED STUDY; PREDICTION; PRIORITY JOURNAL; PROTEIN LOCALIZATION; PROTEIN TARGETING; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

EID: 19544372994     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti372     Document Type: Article

References (28)

1. Aridor,M. and Hannan,L.A. (2000) Traffic jam: a compendium of human diseases that affect intracellular transport processes. Traffic, 1, 836-851.
2. Baldi,P. et al. (1999) Exploiting the past and the future in protein secondary structure prediction. Bioinformatics, 15, 937-946.
3. Bodén,M. and Hawkins,J. (2005) Improved access to sequential motifs: a note on the architectural bias of recurrent networks. IEEE Trans. Neural Networks, 16.
4. Cai,Y.-D. et al. (2002) Artificial neural network model for predicting protein subcellular location. Comput. Chem., 26, 179-182.
5. Cai,Y.-D. et al. (2003) Support vector machines for prediction of protein signal sequences and their cleavage sites. Peptides, 24, 159-161.
6. Chou,K.C. and Cai,Y.D. (2003) Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition. J. Cell. Biochem., 90, 1250-1260.
7. Drawid,A. and Gerstein,M. (2000) A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome. J. Mol. Biol., 301, 1059-1075.
8. Dyrlöv Bendtsen,J. et al. (2004) Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol., 340, 783-795.
9. Emanuelsson,O. (2002) Predicting protein subcellular localisation from amino acid sequence information. Brief. Bioinform., 3, 361-376.
10. Emanuelsson,O. et al. (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol., 300, 1005-1016.
11. Hammer,B. and Tino,P. (2003) Recurrent neural networks with small weights implement definite memory machines. Neural Comp., 15, 1897-1929.
12. Hua,S. and Sun,Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17, 721-728.
13. Käll,L. et al. (2004) A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol., 338, 1027-1036.
14. Lu,Z. et al. (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics, 20, 547-556.
15. Matthews,B.W. (1975) Comparison of predicted and observed secondary structure of t4 phage lysozyme. Biochim. Biophys. Acta, 405, 442-451.
16. Nair,R. and Rost,B. (2003) Better prediction of sub-cellular localization by combining evolutionary and structural information. Proteins, 53, 917-930.
17. Nakai,K. and Horton,P. (1999) PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trend. Biochem. Sci., 24, 34-35.
18. Nielsen,H. et al. (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng., 10, 1-6.
19. Park,K.-J. and Kanehisa,M. (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics, 19, 1656-1663.
20. Pollastri,G. et al. (2002) Improving the prediction of protein secondary strucure in three and eight classes using recurrent neural networks and profiles. Proteins, 47, 228-235.
21. Reinhardt,A. and Hubbard,T. (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res., 26, 2230-2236.
22. Rumelhart,D.E., Durbin,R. Golden,R. and Chauvin,Y. (1995) Backpropagation: the basic theory. In Chauvin,Y. and Rumelhart,D.E. (eds), Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum, Hillsdale, NJ, pp. 1-34.
23. Siegelmann,H.T. (1999) Neural Networks and Analog Computation: Beyond the Turing Limit. Birkhäuser, Basel.
24. Tino,P. et al. (2004) Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Networks, 15, 6-15.
25. van Vliet,C. et al. (2003) Intracellular sorting and transport of proteins. Progr. Biophys. Mol. Biol., 83, 1-45.
26. von Heijne,G. (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res., 14, 4683-4690.
27. Vullo,A. and Frasconi,P. (2004) Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bioinformatics, 20, 653-659.
28. Williams,E.J.B. et al. (2000) The molecular evolution of signal peptides. Gene, 253, 313-322.