Mining association rules from biological databases

Journal of the American Society for Information Science and Technology

Volumn 56, Issue 5, 2005, Pages 493-504

  Rodríguez, Andrés ^a   Carazo, José María ^b   Trelles, Oswaldo ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b CENTRO NACIONAL DE BIOTECNOLOGÍA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DATABASE SYSTEMS; DNA; GENES; KNOWLEDGE ACQUISITION;

BIOLOGICAL DATABASES; LOW-MAGNITUDE SIMILARITY (LMS); MINING ASSOCIATION RULES; PROTEIN SEQUENCES;

DATA MINING;

EID: 15944389020     PISSN: 15322882     EISSN: None     Source Type: Journal    
DOI: 10.1002/asi.20138     Document Type: Article

References (26)

1. Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. Paper presented at the 20th International Conference on Very Large Databases, Santiago, Chile.
2. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In P. Buneman & S. Jajodia (Eds.), Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 207-216). New York: ACM.
3. Agrawal, R., & Shafer, J. (1996). Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, 8(6), 962-969.
4. Agarwal, R.C., Aggarwal, C.C., & Prasad, V.V.V. (2000). Depth first generation of long patterns. In S.J. Simoff & O.R. Zaïarie (Eds.), Proceedings of ACM SIGKDD 2000 (pp. 108-118). New York: ACM.
5. Agarwal, R.C., Aggarwal, C.C., & Prasad, V.V.V. (2001). A tree projection algorithm for generation of frequent itemsets [Special issue]. Journal of Parallel and Distributed Computing, 61(3), 350-371.
6. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhanng, J., Zhang, Z., Miller, W., et al. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein DB search programs. Nucleic Acids Research, 25(17), 3389-3402.
7. Bairoch, A., & Apweiler, R. (2000). The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Research, 28(1), 45-48.
8. Bayardo, R.J. (1998). Efficiently mining long patterns from databases. In L.M. Hass & A. Tiwary (Eds.), Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 85-93). New York: ACM.
9. Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Rapp, B.A., & Wheeler, D.L. (2002). GenBank. Nucleic Acids Research, 30(1), 17-20.
10. Brazma, A., & Vilo, L. (2000). Gene expression data analysis. FEBS Letters, 480, 17-24.
11. Carmona-Saez, P., Chagoyen, M., Rodríguez, A., Trelles, O., Carazo, J.M., & Pascual-Montano, A. (2003, June). Discovering biological knowledge from gene expression using association rules. Paper presented at the 11th International Conference on Intelligent Systems for Molecular Biology, Brisbane, Australia.
12. García de la Nava, J., Franco, D., Cuenca, J., Carazo, J.M., Trelles, O., & Pascual-Montano, A. (2003). Engene: The processing and exploratory analysis of gene expression data. Bioinformatics, 19(5), 657-658.
13. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
14. Gribskov, M., & Devereux, J. (Eds.) (1991). Sequence analysis primer. UWBC Biotechnical Resource Series. New York: Stockton Press.
15. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In W. Chen, J.F. Naughton, & P.A. Bernstein (Eds.), Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1-12). New York: ACM.
16. Liu, J., Pan, Y., Wang, K., & Han, J. (2002, July). Mining frequent item sets by opportunistic projection. Paper presented at SIGKDD 2002, Edmonton, Alberta, Canada.
17. Needleman, S.B., & Wunsch, C.D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443-453.
18. Park, J.S., Chen, M., & Yu, P.S. (1995). An effective hash-based algorithm for mining association rules. In M.J. Carey & D.A. Schneider (Eds.), ACM SIGMOD International Conference on Management of Data (pp. 175-186). New York: ACM.
19. Pearson, W.R., & Lipman, D.J. (1988). Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences USA, 85, 2444-2448.
20. Rodríguez, A., Thode, G., Perez-Pulido, A., Lopez, A.D., Carazo, J.M., & Trelles, O. (2000, July). Mining low-level similarity signals from sequence databases. Paper presented at the 4th Conference on Systematics, Cybernetics and Informatics, Orlando, FL.
21. Sander, Ch., & Schneider, R. (1991). Database of homology derived protein structures and the structural meaning of sequence alignment. Proteins, 9, 56-68.
22. Scott, C.H.P., & Babbit, P. (1999). Shotgun: Getting more from sequence similarity searches. Bioinformatics, 15(9), 729-740.
23. Smith, T.F., & Waterman, M.S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147, 195-197.
24. Trelles, O., Andrade, M.A., Valencia, A., Zapata, L., & Carazo, J.M. (1998). Computational space reduction and parallelization of a new clustering approach for large groups of sequences. BioInformatics, 14(5), 439-451.
25. Zaki, M.J., Partasarathy, S., Ogihara, M., & Li, W. (1996). Parallel data mining for association rules on shared-memory multi-processors. Unpublished manuscript, Computer Science Department TR 618, University of Rochester, New York.
26. Zaki, M.J., Partasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. Unpublished manuscript, Computer Science Department TR 651, University of Rochester, New York.