An artificial immune system for clustering amino acids in the context of protein function classification

Journal of Mathematical Modelling and Algorithms

Volumn 8, Issue 2, 2009, Pages 103-123

  Secker, A ^a   Davies, M N ^b   Freitas, A A ^a   Timmis, J ^c   Clark, E ^c   Flower, D R ^b  

^a UNIVERSITY OF KENT   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Artificial immune systems; Bioinformatics; Classification; Clustering; Optimisation

Indexed keywords

EID: 67349215487     PISSN: 15701166     EISSN: 15729214     Source Type: Journal    
DOI: 10.1007/s10852-009-9107-3     Document Type: Conference Paper

References (32)

1. J. Cui 2007 Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties Mol. Immunol. 44 514 520 (Pubitemid 44308349)
2. J.C. Tong M.T. Tammi 2008 Prediction of protein allergenicity using local descriptions of amino acid sequence Front. Biosci. 13 6072 6078
3. C. Chothia A.V. Finkelstein 1990 The classification and origins of protein folding patterns Ann. Rev. Biochem. 59 1007 1035 (Pubitemid 20207030)
4. A. Christopoulos T. Kenakin 2002 G protein-coupled receptor allosterism and complexing Pharmacol. Rev. 54 323 374 (Pubitemid 34575719)
5. U. Gether 2002 Structural basis for activation of G-protein-coupled receptors Pharm. Toxicol. 91 304 312 (Pubitemid 36132435)
6. C. Bissantz 2003 Conformational changes of G protein-coupled receptors during their activation by agonist binding J. Recept. Signal Transduct. Res. 23 123 153 (Pubitemid 37362236)
7. T.E. Hebert M. Bouvier 1998 Structural and functional aspects of G protein-coupled receptor oligomerization Biochemical Cell Biology 76 1 11 (Pubitemid 29134992)
8. T. Schoneberg 2004 Mutant G-protein-coupled receptors as a cause of human diseases Pharmacol. Ther. 104 173 206 (Pubitemid 39531926)
9. T. Klabunde G. Hessler 2002 Drug design strategies for targeting G-protein coupled receptors ChemBioChem 3 928 944 (Pubitemid 35189597)
10. Jr.L.F. Kolakowski 1994 Gcrdb: A G-protein-coupled receptor database Recept. Channels 2 1 7 (Pubitemid 24216240)
11. T.K. Attwood J.B. Findlay 1993 Design of a discriminating fingerprint for G-protein-coupled receptors Protein Eng. 6 167 176 (Pubitemid 23100796)
12. T.K. Attwood J.B. Findlay 1994 Fingerprinting G-protein-coupled receptors Protein Eng. 7 195 203 (Pubitemid 24031210)
13. M.N. Davies 2007 On the hierarchical classification of G protein-coupled receptors Bioinformatics 23 23 3113 3118 (Pubitemid 350187511)
14. Secker, A., et al.: An experimental comparison of classification algorithms for the hierarchical prediction of protein function. Expert Update (Magazine of the British Computer Society's Specialist Group on AI), Special Issue on the 3rd UK KDD (Knowledge Discovery and Data Mining) Symposium 9(3), 17-22 (2007)
15. M. Sandberg 1998 New chemical descriptors relevant for the design of biologically active peptides. A Multivariate Characterization of 87 Amino Acids J. Med. Chem. 41 14 2481 2491 (Pubitemid 28321898)
16. P. Guan 2005 Analysis of peptide-protein binding using amino acid descriptors: Prediction and experimental verification for human histocompatibility complex Hla-A0201 J. Med. Chem. 48 23 7418 7425 (Pubitemid 41743655)
17. M.N. Davies 2007 Proteomic applications of automated GPCR classification Proteomics 7 16 2800 2814 (Pubitemid 47359922)
18. M.N. Davies 2008 Optimizing amino acid groupings for GPCR classification Bioinformatics 24 18 1980 1986
19. T. Li 2003 Reduction of protein sequence complexity by residue grouping Protein Eng. 16 5 323 330 (Pubitemid 36806923)
20. N. Cannata 2002 Simplifying amino acid alphabets by means of a branch and bound algorithm and substitution matrices Bioinformatics 18 8 1102 1108
21. A. Luthra 2007 A method for computing the inter-residue interaction potentials for reduced amino acid alphabet Biosciences 32 883 889 (Pubitemid 47435520)
22. Freitas, A.A., de Carvalho, A.C.P.L.F.: A tutorial on hierarchical classification with applications in bioinformatics. In: Taniar, D. (ed.) Research and Trends in Data Mining Technologies and Applications, pp. 175-208. Idea Group (2007)
23. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, New York (2002)
24. Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, New York (2002)
25. de Castro, L.N., Timmis, J.: An artificial immune network for multimodal optimisation. In: Proceedings of: 2002 congress on evolutionary computation (CEC 2002). Part of the 2002 IEEE world congress on computational intelligence, pp. 699-704 (2002)
26. Andrews, P.: Opt-Ainet source code in Java. Accessed October 2007 (2005)
27. Andrews, P.S., Timmis, J.: On diversity and artificial immune systems: Incorporating a diversity operator into Ainet. In: Proceedings of: International workshop on natural and artificial immune systems (NAIS), pp. 293-306 (2005)
28. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
29. Brownlee, J.: Weka classification algorithms. Version 1.6. Accessed February 2007 (2006)
30. S.S. Keerthi 2001 Improvements to Platt's SMO algorithm for SVM classifier design Neural Comput. 13 3 637 649 (Pubitemid 33595014)
31. Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of: Fifteenth international conference on machine learning (1998)
32. Watkins, A., Timmis, J.: Artificial immune recognition system (AIRS): Revisions and refinements. In: Proceedings of: 1st International conference on artificial immune systems (ICARIS 2002), pp. 173-181 (2002)