Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding

• Chou, P., & Fasman, G. (1978). Prediction of the secondary structure of proteins from their amino acid sequence.Advances in Enzymology, 47, 45–148.

  Google Scholar 

• Cleeremans, A., Servan-Schreiber, D., & McClelland, J. (1989). Finite state automata and simple recurrent networks.Neural Computation, (3), 372–381.

• Cohen, B., Presnell, S., Cohen, F., & Langridge, R. (1991). A proposal for feature-based scoring of protein secondary structure predictions.Proceedings of the AAAI-91 Workshop on Artificial Intelligence Approaches to Classification and Pattern Recognition in Molecular Biology (pp. 5–20). Anaheim, CA.

• Cost, S., & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features.Machine Learning, 10(1, 57–78.

  Google Scholar 

• Elman, J. (1990). Finding structure in time.Cognitive Science 14(2, 179–211.

  Google Scholar 

• Fahlman, S., & Lebiere, C. (1990). The cascade-correlation learning architecture. In D. Touretzky (Ed.),Advances in neural information processing systems, Vol. 2 (pp. 524–532). Denver, CO: Morgan Kaufmann.

  Google Scholar 

• Fasman, G. (1989). The development of the prediction of protein structure. In Fasman, G. (Ed.),Prediction of protein structure and the principles of protein conformation. New York: Plenum Press.

  Google Scholar 

• Garnier, J., & Robson, B. (1989). The GOR method for predicting secondary structures in proteins. In G. Fasman (Ed.),Prediction of protein structure and the principles of protein conformation. New York: Plenum Press.

  Google Scholar 

• Giles, C., Miller, C., Chen, D., Chen, H., Sun, G., & Lee, Y. (1992). Learning and extracting finite state automata with second-order recurrent neural network.Neural Computation, 4, 393–405.

  Google Scholar 

• Holley, L., & Karplus, M. (1989). Protein structure prediction with a neural network.Proceedings of the National Academy of Science (itUSA), 86, 152–156.

  Google Scholar 

• Hopcroft, J., & Ullman, J. (1979).Introduction to automata theory, languages, and computation. Reading, MA: Addison Wesley.

  Google Scholar 

• Hunter, L. (1991). Representing amino acids with bitstrings.Proceedings of the AAAI-91 Workshop on Artificial Intelligence Approaches to Classification and Pattern Recognition in Molecular Biology (pp. 110–117). Anaheim, CA.

• Jacobs, R., Jordan, M., Nowlan, S., & Hinton, G. (1991). Adaptive mixtures of local experts.Neural Computation, 3(1, 79–87.

  Google Scholar 

• Jordan, M. (1986).Serial order: A parallel distributed processing approach (Technical Report 8604). San Diego: University of California, Institute for Cognitive Science.

  Google Scholar 

• Kneller, D., Cohen, F., & Langridge, R. (1990). Improvements in protein secondary structure prediction by an enhanced neural network.Journal of Molecular Biology, 214, 171–182.

  Google Scholar 

• Lim, V. (1974). Algorithms for prediction of α-helical and β-structural regions in globular proteins.Journal of Molecular Biology, 88, 873–894.

  Google Scholar 

• Mathews, B. (1975). Comparison of the predicted and observed secondary structure of T4 Phage Lysozyme.Biochimica et Biophysica Acta, 405, 442–451.

  Google Scholar 

• Muggleton, S. (Ed.). (1992).Inductive logic programming. London: Academic Press.

  Google Scholar 

• Muggleton, S., & Feng, R. (1991).Predicting protein secondary-structure using inductive logic programming. (Technical Report). Glasgow, Scotland: Turing Institute.

  Google Scholar 

• Nishikawa, K. (1983). Assessment of secondary-structure prediction of proteins: Comparison of computerized Chou-Fasman method with others.Biochimica et Biophysica Acta, 748, 285–299.

  Google Scholar 

• Noordewier, M., Towell, G., & Shavlik, J. (1991). Training knowledge-based neural networks to recognize genes in DNA sequences. In R. Lippmann, J. Moody, & D. Touretzky, (Eds.),Advances in neural information processing systems, Vol. 3. Denver, CO: Morgan Kaufmann.

  Google Scholar 

• Ourston, D., & Mooney, R. (1990). Changing the rules: A comprehensive approach to theory refinement.Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 815–820). Boston, MA: MIT Press.

  Google Scholar 

• Pazzani, M. (1993). Learning causal patterns: Making a transition from data-driven to theory-driven learning.Machine Learning, 11 (this issue).

• Prevelige, P. Jr., & Fasman, G. (1989). Chou-Fasman prediction of the secondary structure of proteins: The Chou-Fasman-Prevelige algorithm. In G. Fasman (Ed.),Prediction of protein structure and the principles of protein conformation. New York: Plenum Press.

  Google Scholar 

• Qian, N., & Sejnowski, T. (1988). Predicting the secondary structure of globular proteins using neural network models.Journal of Molecular Biology, 202, 865–884.

  Google Scholar 

• Quinlan, J. (1990). Learning logical definitions from relations.Machine Learning, 5(3, 239–266.

  Google Scholar 

• Richardson, J., & Richardson, D. (1989). Principles and patterns of protein conformation. In G. Fasman (Ed.),Prediction of protein structure and the principles of protein conformation. New York: Plenum Press.

  Google Scholar 

• Robson, B., & Suzuki, E. (1976). Conformational properties of amino acid residues in globular proteins.Journal of Molecular Biology, 107, 327–356.

  Google Scholar 

• Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning internal representations by error propagation. In D. Rumelhart, & J. McClelland (Eds.),Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations (pp. 318–363). Cambridge, MA: MIT Press.

  Google Scholar 

• Saitta, L., & Botta, M. (1993). Multistrategy learning and theory revision.Machine Learning, 11 (this issue).

• Sejnowski, T., & Rosenberg, C. (1987). Parallel networks that learn to pronounce English text.Complex Systems, 1, 145–168.

  Google Scholar 

• Stolorz, P., Lapedes, A., & Xia, Y. (1991). Predicting protein secondary structure using neural net and statistical methods.Journal of Molecular Biology, 225, 363–377.

  Google Scholar 

• Tecuci, G. (1993). Plausible justification trees: A framework for deep and dynamic integration of learning strategies.Machine Learning, 11 (this issue).

• Towell, G., Shavlik, J., & Noordewier, M. (1990). Refinement of approximate domain theories by knowledge-based neural networks.Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 861–866). Boston, MA: MIT Press.

  Google Scholar 

• Towell, G. (1991).Symbolic knowledge and neural networks: Insertion, refinement and extraction. Doctoral dissertation, Department of Computer Science, University of Wisconsin, Madison.

  Google Scholar 

• Towell, G., & Shavlik, J. (1992). Interpretation of artificial neural networks: Mapping knowledge-based neural networks into rules. In R. Lippmann, J. Moody, & D. Touretzky (Eds.),Advances in neural information processing systems, Vol. 4. Denver, CO: Morgan Kaufmann.

  Google Scholar 

• Watson, J. (1990). The Human Genome Project: Past, present, and future.Science, 248, 44–48.

  Google Scholar 

• Wilson, I., Haft, D., Getzoff, E., Tainer, J., Lerner, R., & Brenner, S. (1985). Identical short peptide sequences in unrelated proteins can have different conformations: A testing ground for theories of immune recognition.Proceeding of the National Academy of Sciences (itUSA), 82, 5255–5259.

  Google Scholar 

• Zhang, X., Mesirov, J. & Waltz, D. (1992). Hybrid system for protein secondary structure prediction.Journal of Molecular Biology, 225, 1049–1063.

  Google Scholar 

Download references