Database support for 3D-protein data set analysis

Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM

Volumn 2003-January, Issue , 2003, Pages 161-170

  Hinneburg, A ^a   Lehner, W ^b  

^a MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Amino acids; Association rules; Bioinformatics; Data analysis; Database systems; Genomics; Proteins; Relational databases; Spatial databases; Yarn

Indexed keywords

AMINO ACIDS; ASSOCIATION RULES; BIOINFORMATICS; DATA REDUCTION; DATABASE SYSTEMS; MANAGEMENT INFORMATION SYSTEMS; PROTEINS; YARN;

AMINO ACID RESIDUES; ASSOCIATION RULE ALGORITHM; COMPREHENSIVE ANALYSIS; GENOMICS; IN-DEPTH ANALYSIS; MULTI DIMENSIONAL; RELATIONAL DATABASE; SPATIAL DATABASE;

RELATIONAL DATABASE SYSTEMS;

EID: 33750845464     PISSN: 10993371     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSDM.2003.1214977     Document Type: Conference Paper

References (14)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Proceedings of the International Conference on Management of Data, pages 207-216. ACM Press, 1993.
2. S. Bohl, M. Dinkelacker, J. Griese, and S. Schrader. Highly adaptable amino acid side chain rotamer library in pdb coordinates. In Workshop in Computational Biology at the Plant Biochemistry Department of the Albert-Ludwigs-Universitt Freiburg, Germany, 2002.
3. M. Bower, F. Cohen, and R. Dunbrack. Homology modeling with a backbone-dependent rotamer library. J. Mol. Biol., 267:1268-1282, 1997.
4. R. Chandrasekaran and G. Ramachandran. Studies on the conformation of amino acids. xi. analysis of the observed side group conformations in proteins. Int. J. Pept. Prot. Res., 2:223-233, 1970.
5. R. Dunbrack and F. Cohen. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci., 6:1661-1681, 1997.
6. J. Gray, A. Bosworth, A. Layman, and H. Pirahesh. Data cube: A relational aggregation operator generalizing groupby, cross-tab, and sub-total. In Proceedings of the Twelfth International Conference on Data Engineering, pages 152-159. IEEE Computer Society, 1996.
7. A. Hinneburg, M. Fischer, and F. Bahner. Finding frequent substructures in 3d-protein databases. In Workshop on Bioinformatics at the 19th International Conference on Data Engineering. IEEE Computer Society, 2003.
8. M. James and A. Sielecki. Structure and refinement of penicillo-pepsin at 1.8 a resolution. J. Mol. Biol., 125:299-361, 1983.
9. J. Kuszewski, A. Gronenborn, and G. Clore. Improving the quality of nmr and crystallographic protein structures by means of conformational database potential derived from structure databases. Protein Sci., 5:1067-1080, 1996.
10. S. C. Lovell, J. M. Word, J. S. Richardson, and D. C. Richardson. The penultimate rotamer library. Proteins: Struct Funct Genet, 40:389-408, 2000.
11. M. MCGregor, S. Islam, and M. Sternberg. Analysis of the relationship between side-chain conformation and secondary structure in globular proteins. J. Mol. Biol., 198:295-310, 1987.
12. R. Srikant and R. Agrawal. Mining generalized association rules. In VLDB'95, Proceedings of 21th International Conference on Very Large Data Bases, Switzerland, pages 407-419. Morgan Kaufmann, 1995.
13. M. J. Zaki. Efficient enumeration of frequent sequences. In Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, Bethesda, Maryland, USA, November 3-7, 1998, pages 68-75. ACM, 1998.
14. M. Zhang, B. Kao, C. L. Yip, and D. W.-L. Cheung. Ffs - an i/o-efficient algorithm for mining frequent sequences. In Knowledge Discovery and Data Mining - PAKDD 2001, 5th Pacific-Asia Conference, Hong Kong, China, April 16-18, 2001, Proceedings, volume 2035 of Lecture Notes in Computer Science, pages 294-305. Springer, 2001.