Geometric suffix tree: A new index structure for protein 3-D structures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4009 LNCS, Issue , 2006, Pages 84-93

  Shibuya, Tetsuo ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL GEOMETRY; DATA STRUCTURES; LINEAR SYSTEMS; PROTEINS; VECTORS;

GEOMETRIC SUFFIX; PROTEIN 3-D STRUCTURES; PROTEIN STRUCTURE ANALYSIS; SUBSTRUCTURES;

THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 33746038552     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11780441_9     Document Type: Conference Paper

References (20)

1. T. Akutsu, K. Onizuka, and M. Ishikawa. New hashing techniques and their application to a protein database system. Proc. Hawaii Int. Conf. System Sciences (HICSS-28), Vol. 5, pp. 197-206, 1995.
2. K. S. Arun, T. S. Huang, and S. D. Blostein. Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal. Machine Intell., Vol. 9, pp. 698-700, 1987.
3. Z. Aung, W. Fu and K. Tan. An efficient index-based protein structure database searching method. Proc. Intl. Conf. on Database Systems for Advanced Applications, pp. 311-318, 2003.
4. H. M. Berman, J. Westbrook, Z. Feng, et al. The protein data bank. Nucl. Acids Res., Vol. 28, pp. 235-242, 2000.
5. O. Çamoǧlu, T. Kahveci and A. Singh. Towards index-based similarity search for protein structure databases. IEEE Computer Society Bioinformatics Conference, pp. 148-158, 2003.
6. T. Can and Y. Wang. CTSS: a robust and efficient method for protein structure alignment based on local geometrical and biological features. IEEE Computer Society Bioinformatics Conference pp. 169-179, 2003.
7. L. P. Chew, D. Huttenlocher, K. Kedem and J. Kleinberg. Fast detection of common geometric substructure in proteins. J. Comput. Biol., Vol. 6, No. 3, pp. 313-325, 1999.
8. I. Choi, J. Kwon and S. Kim, Local feature frequency profile: A method to measure structural similarity in proteins. Proc. Natl. Acad. Sci., Vol. 101, No. 11, pp, 3797-3802, 2004.
9. D. W. Eggert, A. Lorusso and R. B. Fisher. Estimating 3-D rigid body transformations: a comparison of four major algorithms. Machine Vision and Applications, Vol. 9, pp. 272-290, 1997.
10. I. Eidhammer, I. Jonassen, and W. R. Taylor. Structure Comparison and Structure Patterns. J. Computational Biology, Vol. 7, No. 5, pp. 685-716, 2000.
11. M. Farach. Optimal suffix tree construction with large alphabets. Proc. 38th IEEE Symp. Foundations of Computer Science, pp. 137-143, 1.997.
12. F. Gao and M. J. Zaki. PSIST: Indexing Protein Structures using Suffix Trees. Proc. IEEE Computational Systems Bioinformatics Conference (CSB), pp. 212-222, 2005.
13. G. H. Golub and C. F. Van Loan. Matrix Computation. 3rd eds., John Hopkins University Press, 1996.
14. D. Gusfield. Algorithms on strings, trees, and sequences: computer science and computational biology, Cambridge University Press, 1997.
15. K. Kedem, P. Chew and R. Elber. Unit-vector RMS (URMS) as a tool to analyze molecular dynamics trajectories. Proteins: Struct. Funct. Genet., Vol. 38, pp. 1-12, 1999.
16. E. M. McCreight, A space-economical suffix tree construction algorithm. J. ACM., Vol. 23, pp. 262-272, 1976.
17. J. T. Schwartz and M. Sharir, Identification of partially obscured objects in two and three dimensions by matching noisy characteristic curves. Intl. J. of Robotics Res., Vol. 6, pp. 29-44, 1987.
18. T. Shibuya. Generalization of a suffix tree for RNA structural pattern matching. Algorithmica, Vol. 39, No. 1, pp. 1-19, 2004.
19. E. Ukkonen. On-line construction of suffix-trees. Algorithmica, Vol. 14, pp. 249-260, 1995.
20. P. Weiner. Linear pattern matching algorithms. Proc. 14th Symposium on Switching and Automata Theory, pp. 1-11, 1973.