Feature selection and combination criteria for improving accuracy in protein structure prediction

IEEE Transactions on Nanobioscience

Volumn 6, Issue 2, 2007, Pages 186-196

  Lin, Ken Li ^a,b   Lin, Chun Yuan ^c   Huang, Chuen Der ^d   Chang, Hsiu Ming ^c   Yang, Chiao Yun ^e   Lin, Chin Teng ^a   Tang, Chuan Yi ^c   Hsu, D Frank ^f  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b CHUNG HUA UNIVERSITY   (Taiwan)

^c NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^d HSIUPING INSTITUTE OF TECHNOLOGY   (Taiwan)

^e BenQ Corporation   (Taiwan)

^f FORDHAM UNIVERSITY   (United States)

Author keywords

Combinatorial fusion analysis (CFA); Data fusion; Diversity rank score graph; Hierarchical learning architecture (HLA); Neural network (NN); Protein structure prediction; Radical basis function network (RBFN); Rank score functions

Indexed keywords

BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); COMBINATORIAL MATHEMATICS; DATA FUSION; DATABASE SYSTEMS; FUNCTION EVALUATION; HIERARCHICAL SYSTEMS; LEARNING SYSTEMS; NEURAL NETWORKS;

COMBINATORIAL FUSION ANALYSIS (CFA); DIVERSITY RANK/SCORE GRAPH; HIERARCHICAL LEARNING ARCHITECTURE (HLA); PROTEIN STRUCTURE PREDICTION; RADICAL BASIS FUNCTION NETWORK (RBFN); RANK/SCORE FUNCTIONS;

PROTEINS;

PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; COMPUTER INTERFACE; COMPUTER PREDICTION; PROTEIN DOMAIN; PROTEIN FOLDING; PROTEIN STRUCTURE; REFERENCE DATABASE; STRUCTURE ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, PROTEIN; INFORMATION STORAGE AND RETRIEVAL; MODELS, CHEMICAL; MODELS, MOLECULAR; PATTERN RECOGNITION, AUTOMATED; PROTEINS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, PROTEIN;

EID: 34249989677     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2007.897482     Document Type: Article

References (33)

1. L. Lo Conte, B. Ailey, T. J. P. Hubbard, S. E. Brenner, A. G. Murzin, and C. Chothia, "SCOP: A structural classification of proteins database," Nucleic Acids Res., vol. 28, no. 1, pp. 257-259, 2000.
2. F. M. Pearl, D. Lee, J. E. Bray, I. Sillitoe, A. E. Todd, A. P. Harrison, J. M. Thornton, and C. A. Orengo, "Assigning genomic sequences to CATH," Nucleic Acids Res., vol. 28, no. 2, pp. 584-599, 2000.
3. A. Antonina, H. Dave, E. B. Steven, J. P. H. Tim, C. Cyrus, and G. M. Alexey, "SCOP database in 2004: Refinements integrate structure and sequence family data," Nucleic Acids Res., vol. 32, pp. 226-229, 2004.
4. I. Dubchak, I. Muchnik, S. R. Holbrook, and S. H. Kim, "Prediction of protein folding class using global description of amino acid sequence," Proc. Nat. Acad. Sci. USA, vol. 92, pp. 8700-8704, 1995.
5. K. C. Chou and C. T. Zhang, "Prediction of protein structural classes," Crit. Rev. Biochem. Mol. Biol., vol. 30, no. 4, pp. 275-349, 1995.
6. A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia, "SCOP: A structural classification of proteins database for the investigation of sequence and structures," J. Mol. Biol., vol. 247, pp. 536-540, 1995.
7. C. H. Q. Ding and I. Dubchak, "Multi-class protein fold recognition using support vector machines and neural networks," Bioinformatics, vol. 17, no. 4, pp. 349-358, 2001.
8. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
9. C. D. Huang, C. T. Lin, and N. R. Pal, "Hierarchical learning architecture with automatic feature selection for multi-class protein fold classification," IEEE Trans. NanoBiosci., vol. 2, no. 4, pp. 503-517, Dec. 2003.
10. D. F. Hsu, J. Shapiro, and I. Taksa, Methods of data fusion in information retreival: Rank vs. score combination DIMACS Tech. Rep. 58, 2002.
11. D. F. Hsu and I. Taksa, "Comparing rank and score combination methods for data fusion in information retrieval," Inf. Retr., vol. 8, pp. 449-480, 2005.
12. J.-M. Yang, Y.-F. Chen, T.-W. Shen, B. S. Kristal, and D. F. Hsu, "Consensus scoring criteria for improving enrichment in virtual screening," J. Chem. Inf. Model., vol. 45, pp. 1134-1146, 2005.
13. D. F. Hsu, Y.-S. Chung, and B. S. Kristal, "Combinatorial fusion analysis: Method and practice of combining multiple scoring systems," in Advanced Data Mining Technologies in Bioinformatics, H.-H. Hsu, Ed. Hershey, PA: Idea Group Inc., 2006, pp. 32-62.
14. K. B. Ng and P. B. Kantor, "Predicting the effectiveness of naïve data fusion on the basis of system characteristics," J. Amer. Soc. Inf. Sci., vol. 51, no. 13, pp. 1177-1189, 2000.
15. I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S. H. Kim, "Recognition of a protein fold in the context of the SCOP classification," Proteins, vol. 35, pp. 401-407, 1999.
16. L. L. Conte, S. E. Brenner, T. J. Hubbard, S. E. Brenner, A. G. Murzin, and C. Chothia, "SCOP database in 2002: Refinements accommodate structural genomics," Nucleic Acids Res., vol. 30, no. 1, pp. 264-267, 2002.
17. P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Approach. Cambridge, MA: MIT Press.
18. C. H. Wu, Neural Networks and Genome Informatics. Amsterdam, The Netherlands: Elsevier, 2000.
19. J. Moody and C. J. Darken, "Fast learning in networks of locally tuned processing units," Neural Comput., vol. 1, no. 2, pp. 281-294, 1989.
20. N. J. Belken, P. B. Kantor, E. A. Fox, and J. A. Shaw, "Combining evidence of multiple query representation for information retrieval," Inf. Process. Manage., vol. 31, no. 3, pp. 431-448, 1995.
21. C. C. Vogt and G. W. Cotrell, "Fusion via a linear combination of scores," Inf. Retr., vol. 1, pp. 151-172, 1999.
22. L. Xu, A. Krzyzak, and C. Y. Suen, "Method of combining multiple classifiers and their application to handwriting recognition," IEEE Trans. Syst., Man, Cybern., vol. 22, no. 3, pp. 418-435, May/Jun. 1992.
23. C. M. R. Ginn, P. Willett, and J. Bradshaw, "Combination of molecular similarity measures using data fusion," Perspectives Drug Discov. Des., vol. 20, pp. 1-16, 2000.
24. M. A. Kuriakose, W. T. Chen, Z. M. He, A. G. Sikora, P. Zhang, Z. Y. Zhang, W. L. Qiu, D. F. Hsu, C. M. Coffran, S. M. Brown, E. M. Elango, M. D. Delacure, and F. A. Chen, "Selection and validation of differentially expressed genes in head and neck cancer," Cell. Mol. Life Sci., vol. 61, pp. 1372-1383, 2004.
25. H. Y. Chuang, H. F. Liu, S. Brown, C. M. Coffran, and D. F. Hsu, "Identifying significant genes from microarray data," in Proc. IEEE Symp. Bioinformatics and Bioengineering (BIBE'04), pp. 358-365.
26. H. Y. Chuang, H. F. Liu, F. A. Chen, C. Y. Kao, and D. F. Hsu, "Combination methods in microarray analysis," in Proc. 7th Int. Symp. Parallel Architectures, Algorithms and Networks (I-SPAN '04), pp. 625-630.
27. D. F. Hsu and A. Palumbo, "A study of data fusion in Cayley graphs G (Sn, Pn)," in Proc. 7th Intl. Symp. Parallel Architectures, Algorithms and Networks (I-SPAN '04), pp. 557-562.
28. B. Rost and C. Sander, "Prediction of protein secondary structure at better than 70% accuracy," J. Mol. Biol., vol. 232, pp. 584-599, 1993.
29. C. S. Yu, J. Y. Wang, J. M. Yang, P. C. Lyu, C. J. Lin, and J. K. Hwang, "Fine-grained protein fold assignment by support vector machines using generalized n peptide coding schemes and jury voting from multiple-parameter sets," Proteins, vol. 50, pp. 531-536, 2003.
30. K.-L. Lin, C. Y. Lin, C.-D. Huang, H.-M. Chang, C. Y. Yang, C.-T. Lin, C. Y. Tang, and D. F. Hsu, "Methods of improving protein structure prediction based on HLA neural network and combinatorial fusion analysis," WSEAS Trans. Inf. Sci. Appl., vol. 2, no. 12, pp. 2146-2153, 2005.
31. J. Jundstrom, L. Rychlewski, J. Bujnicki, and A. Elofsson, "Pcons: A neural-network-based consensus predictor that improves fold recognition," Protein Sci., vol. 10, pp. 2354-2362, 2001.
32. L. J. McGriffin and D. T. Jones, "Improvement of the GenThreader method for genome fold recognition," Bioinformatics, vol. 19, no. 7, pp. 874-881, 2003.
33. P. Daras, D. Zarpalas, A. Axenopoulos, D. Tzovaras, and M. G. Strintzis, "Three-dimensional shape-structure comparison method for protein classification," IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 3, no. 3, pp. 193-207, 2006.