DATA
ProteinData
- Protein Data Base. Each protein is represented by its sequence of amino acids and distances between them.
CODE
SourceCode
- Java Source Code for the protein data treatment.
BIBLIOGRAPHY
1. Fariselli, P., Casadio, R., 1999. A neural network based predictor of residue contacts in proteins. Protein Engineering, Vol. 12, No. 1, 15-21.
2. Fariselli, P., Olmea, O., Valencia, A., Casadio, R., 2001. Prediction of contact maps with neural networks and correlated mutations. Protein Engineering. 14(11):835-843.
3. Guo, L., Huang, D.S., Zhao, W., 2003. Combining genetic optimisation with hybrid learning algorithm for radial basis function neural networks. IEE Electron. Lett. 39 (22), 1600–1601.
4. Guo, J., Chen, H., Sun, Z., Lin, Y., 2004. A novel method for protein secondary structure prediciton using dual-layer SVM and profiles. Proteins: Struct. Funct. Bioinform. 54, 738–743.
5. Huang, D.S., 1999. Radial basis probabilistic neural networks: Model and application. Int. J. Pattern Recognit. Artif. Intell. 13 (7), 1083–1101.
6. Olmea, O., Rost, B., Valencia, A., 1999. Effective use of sequence correlation and conservation in fold recognition. J. Mol. Biol. 293, 1221–1239.
7. Pollastri, G., Baldi, P., 2002. Prediction of contact maps by recurrent neural network architectures and hidden context propagation from all four cardinal corners. Bioinformatics 18 (Suppl.), S62–S70.
8. Vullo, A., Walsh, I., Pollastri, G., 2006. A two-stage approach for improved prediction of residue contact maps. BMC Bioinformatics 7, 180.
9. Zhang, G.-Z., Huang, D.S., 2004. Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme. J. Comput. Aided Mol. Des. 18, 797–810.
10. Zhang, G.-Z., Huang, D.S., 2005. Combing a binary input encoding scheme with RBFNN for globulin protein inter-residue contact map prediction. Pattern Recognit. Lett. 26, 1543–1553.
11. Walsh, I., Bau, D., Martin, A. JM., Mooney, C., Vullo, A., Pollastri, G., 2009. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks. BMC Structural Biology. 9:5.
12. Bohr, H., Bohr, J., Brunak, S., Cotterill, RM., Lautrup, B., Nørskov, L., Olsen, OH., Petersen, SB., 1988. Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin. FEBS Lett. 1988 Dec 5;241(1-2):223-8.
13. Bohr, H., Bohr, J., Brunak, S., Cotterill, R.M.J, Fredholm, H., Lautrup, B., 1990. A novel approach to prediction of the 3-dimensional structures of protein backbones by neural networks. FEBS Letters 261 (1990), pp. 43–46.