Application of Combining Classifiers Using Dynamic Weights to the Protein Secondary Structure Prediction – Comparative Analysis of Fusion Methods

Author:

Woloszynski Tomasz,Kurzynski Marek

Publisher

Springer Berlin Heidelberg

Reference24 articles.

1. Akkaladevi, S., Balkasim, M., Pan, Y.: Protein Secondary Structure Prediction Using Neural Network and Simulated Annealing Algorithm. In: Proc. 26th Int. Conf. of the IEEE EMBS, pp. 2987–2990. San Francisco (2004)

2. Aydin, Z., Altunbasak, Y., Borodovsky, M.: Protein Secondary Structure Prediction with Semi Markov HMMS. In: Proc. 26th Int. Conf. of the IEEE EMBS, pp. 2964–2967. San Francisco (2004)

3. Bermann, H., Westbrock, Z., Feng, G., et al.: The Protein Data Bank. Nucleic Acid Res. 2, 235–242 (2000)

4. Bridle, J.: Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. In: Touretzky, D. (ed.) Neural Information Processing Systems, vol. 2, pp. 211–217. Morgan Kaufmann, San Mateo, CA (1990)

5. Combet, C., Blanchet, C., Geourjon, C., Deleage, G.: NPS@: Network Protein Sequence Analysis. TIBS 25(3), 147–150 (2000)

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