Prediction of residue exposure and contact number for simplified HP lattice model proteins using learning classifier systems

Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006

Volumn , Issue , 2006, Pages 601-608

  Stout, Michael ^a   Bacardit, Jaume ^a   Hirst, Jonathan D ^a   Blazewicz, Jacek ^b   Krasnogor, Natalio ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; LEARNING SYSTEMS; PROTEINS;

CUBIC LATTICE; DIAMOND LATTICES; HP LATTICE; HP MODEL; LATTICE MODELING; LEARNING CLASSIFIER SYSTEM; PROBLEM DOMAIN;

CRYSTAL LATTICES;

EID: 46949108811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1142/9789812774118_0085     Document Type: Conference Paper

References (14)

1. Yue, K, Fiebig, KM., Thomas, P.D., Sun, C.H., Shakhnovich, E.I., Dill, KA.: A test of lattice protein folding algorithms. Proc. Natl. Acad. Sci. USA 92 (1995) 325-329
2. Stout, M., Bacardit, J., Hirst, J.D., Krasnogor, N., Blazewicz, J.: From hp lattice models to real proteins: coordination number prediction using learning classifier systems. In: 4th European Workshop on Evolutionary Computation and Machine Learning in Bioinformatics 2006 (to appear). (2006)
3. Blazewicz, J., Dill, K, Lukasiak, P., Milostan, M.: A tabu search strategy for finding low energy structures of proteins in hp-model. (Computational Methods in Science and Technology)
4. Blazewicz, J., Lukasiak, P., Milostan, M.: Application of tabu search strategy for finding low energy structure of protein. Artificial Intelligence in Medicine 35 (2005) 135-145
5. Hirst, J.D.: The evolutionary landscape of functional model proteins. Protein Engineering 12 (1999) 721-726
6. Dill, K., Bromberg, S., Yue, K., Fiebig, K., Yee, D., Thomas, P., Chan, H.: Principles of protein folding: A perspective from simple exact models. Prot. Sci. 4 (1995) 561
7. Krasnogor, N., Blackburne, B., Burke, E., Hirst, J.: Multimeme algorithms for protein structure prediction. In: Proceedings of the Parallel Problem Solving from Nature VII. Lecture Notes in Computer Science. Volume 2439. (2002) 769-778
8. Hart, W., Istrail, S.: Fast protein folding in the hydrophobic-hydrophilic model within three-eighths of optimal. Journal of Computational Biology 3 (1996) 53-96
9. Bacardit, J.: Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain (2004)
10. DeJong, K, Spears, W., Gordon, D.: Using genetic algorithms for concept learning. Machine Learning 13 (1993) 161-188
11. Blackburne, B.P., Hirst, J.D.: Three dimensional functional model proteins: Structure, function and evolution. Journal of Chemical Physics 119 (2003) 3453-3460
12. Hart, W.: (www.cs.sandia.gov/tech-feports/compbio/tortilla-hpbenchmarks.html) Tortilla HP Benchmarks.
13. Blackburne, B.P., Hirst, J.D.: Evolution of functional model proteins. Journal of Chemical Physics 115 (2001) 1935-1942
14. Miller, R.G.: Simultaneous Statistical Inference. Springer Verlag, New York (1981) Heidelberger, Berlin.