Differential association rules: Understanding annotations in protein interaction networks

Advanced Data Mining Technologies in Bioinformatics

Volumn , Issue , 2006, Pages 269-282

  Besemann, Christopher ^a   Denton, Anne ^a   Yekkirala, Ajay ^a   Hutchison, Ron ^b   Anderson, Marc ^a  

^a NORTH DAKOTA STATE UNIVERSITY   (United States)

^b RICHARD STOCKTON COLLEGE OF NEW JERSEY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34250893549     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-863-5.ch014     Document Type: Chapter

References (30)

1. Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, DC (pp. 207-216).
2. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., & Lakhal, L. (2000). Mining minimal non-redundant association rules using frequent closed itemsets. Lecture Notes in Computer Science, 1861(972).
3. Besemann, C., Denton, A., Yekkirala, A., Hutchison, R., & Anderson, M. (2004). Differential association rule mining for the study of protein-protein interaction networks. Proceedings of the Workshop on Data Mining in Bioinformatics at KDD, Seattle, WA.
4. Brin, S., Motwani, R., Ullman J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. Proceedings ACM SIGMOD International Conference on Management of Data, Tucson, AZ.
5. Cristofor, L., & Simovici, D. (2001). Mining association rules in entity-relationship modeled databases. Technical report, University of Massachusetts Boston.
6. CYGD. (2004). Retrieved March, 2004 from http://mips.gsf.de/genre/proj/yeast/index.jsp.
7. Dehaspe, L., & Raedt, L. D. (1997). Mining association rules in multiple relations. Proceedings of the 7th International Workshop on Inductive Logic Programming, Prague, Czech Republic (pp. 125-132).
8. Elmasri, R. & Navathe, S. (2004). Fundamentals of Database Systems. Boston: Pearson.
9. Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C. J., Hofmann, K., et al. (2002). The prosite database, its status in 2002. Nucleic Acids Research, 30, 235-238.
10. Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large databases. Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Switzerland.
11. Ikura Lab, O. C. I. (2004). Domain fusion database. Retrieved March, 2004 from http://calcium.uhnres.utoronto.ca/pi/pubpages/download/index.htm.
12. Inokuchi, A., Washio, T., & Motoda, H. (2000). An apriori based algorithm for mining frequent substructures from graph data. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France (pp. 13-23).
13. Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001). A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA, 98(8), 4569-74.
14. Jensen, D., & Neville, J. (2002). Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. Lecture Notes in Artificial Intelligence, 2583, 101-116.
15. Marcotte, E. M., Pellegrini, M., Ng, H. L., Rice, D. W., Yeates, T. O., & Eisenberg, D. (1999). Detecting protein function and protein-protein interactions from genome sequences. Science, 285(5428), 751-3.
16. Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910-3.
17. Mewes, H., Frishman, D., Gldener, U., Mannhaupt, G., Mayer, K., Mokrejs, M., et al. (2002). Mips: A database for genomes and protein sequences. Nucleic Acids Research, 30(1), 31-44.
18. Michihiro, K., & Karypis, G. (2001). Frequent subgraph discovery. Proceedings of the International Conference on Data Mining. San Jose, CA (pp. 313-320).
19. Oyama, T., Kitano, K., Satou, K., & Ito, T. (2002). Extraction of knowledge on protein-protein interaction by association rule discovery. Bioinformatics, 18(5), 705-14.
20. Ozier, O., Amin, N., & Ideker, T. (2003). Genetic interactions: Online supplement to: Global architecture of genetic interactions on the protein network. Retrieved January 31, 2005, from http://web.wi.mit.edu/ideker/pub/nbt/.
21. Ozier, O., Amin, N., & Ideker, T. (2003). Global architecture of genetic interactions on the protein network [Letter to the editor]. Nature Biotechnology, 21(5), 490-1.
22. Pei, J., Han, J., & Lakshmanan, L.V. (2004). Pushing convertible constraints in frequent itemset mining. Data Mining and Knowledge Discovery, 8, 227-252.
23. Schwikowski, B., Uetz, P., & Fields, S. (2000). A network of protein-protein interactions in yeast. Nature Biotechnology, 18(12), 1242-3.
24. Srikant, R., Vu, Q., & Agrawal, R. (1997). Mining association rules with item constraints. Proceedings of the International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, (pp. 67-73).
25. Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pag, N., et al. (2001). Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science, 294(5550), 2364-8.
26. Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pag, N., et al. (2004). Global mapping of the yeast genetic interaction network. Science, 303(5695), 808-815.
27. Truong, K., & Ikura, M. (2003). Domain fusion analysis by applying relational algebra to protein sequence and domain databases. BMC Bioinformatics, 4(16).
28. Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., et al. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-7.
29. Wagner, A., & Fell, D. (2001). The small world inside large metabolic networks. Proc. Roy. Soc. London Series B, 268 (pp. 1803-1810).
30. Zaki, M. J. (2000). Generating non-redundant association rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston (pp. 34-43).