Coordination number prediction using learning classifier systems: Performance and interpretability

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 1, Issue , 2006, Pages 247-254

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

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Bioinformatics; Coordination Number Prediction; Evolutionary Algorithms; Learning Classifier Systems; Protein Structure Prediction; Rule Induction

Indexed keywords

AMINO ACIDS; CONFORMAL MAPPING; CONFORMATIONS; EVOLUTIONARY ALGORITHMS; PROTEINS;

COORDINATION NUMBER (CN); INTERPRETABILITY; LEARNING CLASSIFIER SYSTEMS (LCS); PROTEIN STRUCTURE;

LEARNING SYSTEMS;

EID: 33750247224     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. S. F. Altschul, T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res, 25:3389-3402, 1997.
2. J. Bacardit. Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain, 2004. http://www.cs.nott.ac.uk/ jqb/publications/thesis.pdf.
3. J. Bacardit. Analysis of the initialization stage of a Pittsburgh approach learning classifier system. In GECCO 2005: Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1843-1850. ACM Press, 2005.
4. J. Bacardit, D. Goldberg, M. Butz, X. Llorà, and J. M. Garrell. Speeding-up Pittsburgh learning classifier systems: Modeling time and accuracy. In Parallel Problem Solving from Nature - PPSN 2004, pages 1021-1031. Springer-Verlag, LNCS 3242, 2004.
5. P. Baldi and G. Pollastri. The principled design of large-scale recursive neural network architectures dag-rnns and the protein structure prediction problem. Journal of Machine Learning Research, 4:575 - 602, 2003.
6. L. Booker. Recombination distribution for genetic algorithms. In Foundations of Genetic Algorithms 2, pages 29-44. Morgan Kaufmann, 1993.
7. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
8. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. Department of Computer Science and Information Engineering, National Taiwan University, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
9. K. A. DeJong, W. M. Spears, and D. F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13(2/3): 161-188, 1993.
10. J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
11. G. H. John and P. Langley. Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338-345. Morgan Kaufmann Publishers, San Mateo, 1995.
12. D. Jones. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol, 292:195-202, 1999.
13. A. R. Kinjo, K. Horimoto, and K. Nishikawa. Predicting absolute contact numbers of native protein structure from amino acid sequence. Proteins, 58:158-165, 2005.
14. H. Liu, F. Hussain, C. L. Tam, and M. Dash. Discretization: An enabling technique. Data Mining and Knowledge Discovery, 6(4):393-423, 2002.
15. C. D. Livingstone and G. J. Barton. Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation. Computer Applications in the Biosciences, 9(6):745-756, 1993.
16. R. MacCallum. Striped sheets and protein contact prediction. Bioinformatics, 20:I224-I231, 2004.
17. R. G. Miller. Simultaneous Statistical Inference. Springer Verlag, New York, 1981. Heidelberger, Berlin.
18. T. Noguchi, H. Matsuda, and Y. Akiyama. Pdb-reprdb: a database of representative protein chains from the protein data bank (pdb). Nucleic Acids Res, 29:219-220, 2001.
19. J. R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann, 1993.
20. J. Rissanen. Modeling by shortest data description. Automatica, vol. 14:465-471, 1978.
21. C. Sander and R. Schneider. Database of homology-derived protein structures. Proteins, 9:56-68, 1991.
22. Y. Shao and C. Bystroff. Predicting interresidue contacts using templates and pathways. Proteins, 53:497-502, 2003.
23. M. Stout, J. Bacardit, J. Hirst, N. Krasnogor, and J. Blazewicz. 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.
24. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2): 149-175, 1995.
25. I. H. Witten and E. Frank. Data Mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, 2000.
26. Y. Zhao and G. Karypis. Prediction of contact maps using support vector machines. In Proc. of the IEEE Symposium on Bioinformatics and Bio Engineering, pages 26-36. IEEE Computer Society, 2003.