A primer to frequent itemset mining for bioinformatics
- Published: 26 Oct 2013 Journal: Briefings in Bioinformatics, volume 16, pages 216-231 (issn: 1467-5463, eissn: 1477-4054,
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- Country: Belgium
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- University of Antwerp Belgium
Agrawal, R, Imieliński, T, Swami, A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 1993. ; Vol. 22: 207-16 [OpenAIRE]
Carmona-Saez, P, Chagoyen, M, Rodriguez, A. Integrated analysis of gene expression by association rules discovery. BMC Bioinformatics. 2006; 7: 54 [OpenAIRE] [PubMed]
Manda, P, Ozkan, S, Wang, H. Cross-ontology multi-level association rule mining in the gene ontology. PLoS One. 2012; 7: e47411 [OpenAIRE] [PubMed]
Koyutürk, M, Kim, Y, Subramaniam, S. Detecting conserved interaction patterns in biological networks. J Comput Biol. 2006; 13: 1299-322 [OpenAIRE] [PubMed]
Yoon, Y, Lee, GG. Subcellular localization prediction through boosting association rules. IEEE/ACM Trans Comput Biol Bioinform. 2012; 9: 609-18 [PubMed]
Agrawal, R, Srikant, R, Bocca, JB, Jarke, M, Zaniolo, C. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference. 1994: 487-99
Goethals, B, Maimon, O, Rokach, L. Frequent Set Mining. The Data Mining and Knowledge Discovery Handbook. 2010: 321-38
Tan, P-N, Steinbach, M, Kumar, V. Chapter 6. Association analysis: basic concepts and algorithms. Introduction to Data Mining. 2005: 769
Antonie, ML, Zaïane, OR, Boulicaut, JF, Esposito, F, Giannotti, F, Pedreschi, D. Mining positive and negative association rules: an approach for confined rules. Knowledge Discovery in Databases: PKDD 2004. 2004: 27-38 [OpenAIRE]
Besson, J, Boulicaut, JF, Guns, T, Nijssen, S, Džeroski, S, Goethals, B, Panov, P. Generalizing itemset mining in a constraint programming setting. Inductive Databases and Constraint-Based Data Mining. 2010: 107-26
Tan, P-N, Kumar, V, Srivastava, J. Selecting the right interestingness measure for association patterns. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002. : 32-41
Franceschini, A, Szklarczyk, D, Frankild, S. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2012; 41: D808-15 [OpenAIRE] [PubMed]
Zaki, M, Parthasarathy, S, Ogihara, M, Li, W. New algorithms for fast discovery of association rules. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD). Newport Beach, CA, USA, 1997. : 283-6 [OpenAIRE]
Han, J, Pei, J, Yin, Y. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov. 2004; 8: 53-87
Artamonova, II, Frishman, G, Gelfand, MS. Mining sequence annotation databanks for association patterns. Bioinformatics. 2005; 21: iii49-57 [OpenAIRE] [PubMed]
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- University of Antwerp Belgium
Agrawal, R, Imieliński, T, Swami, A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 1993. ; Vol. 22: 207-16 [OpenAIRE]
Carmona-Saez, P, Chagoyen, M, Rodriguez, A. Integrated analysis of gene expression by association rules discovery. BMC Bioinformatics. 2006; 7: 54 [OpenAIRE] [PubMed]
Manda, P, Ozkan, S, Wang, H. Cross-ontology multi-level association rule mining in the gene ontology. PLoS One. 2012; 7: e47411 [OpenAIRE] [PubMed]
Koyutürk, M, Kim, Y, Subramaniam, S. Detecting conserved interaction patterns in biological networks. J Comput Biol. 2006; 13: 1299-322 [OpenAIRE] [PubMed]
Yoon, Y, Lee, GG. Subcellular localization prediction through boosting association rules. IEEE/ACM Trans Comput Biol Bioinform. 2012; 9: 609-18 [PubMed]
Agrawal, R, Srikant, R, Bocca, JB, Jarke, M, Zaniolo, C. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference. 1994: 487-99
Goethals, B, Maimon, O, Rokach, L. Frequent Set Mining. The Data Mining and Knowledge Discovery Handbook. 2010: 321-38
Tan, P-N, Steinbach, M, Kumar, V. Chapter 6. Association analysis: basic concepts and algorithms. Introduction to Data Mining. 2005: 769
Antonie, ML, Zaïane, OR, Boulicaut, JF, Esposito, F, Giannotti, F, Pedreschi, D. Mining positive and negative association rules: an approach for confined rules. Knowledge Discovery in Databases: PKDD 2004. 2004: 27-38 [OpenAIRE]
Besson, J, Boulicaut, JF, Guns, T, Nijssen, S, Džeroski, S, Goethals, B, Panov, P. Generalizing itemset mining in a constraint programming setting. Inductive Databases and Constraint-Based Data Mining. 2010: 107-26
Tan, P-N, Kumar, V, Srivastava, J. Selecting the right interestingness measure for association patterns. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002. : 32-41
Franceschini, A, Szklarczyk, D, Frankild, S. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2012; 41: D808-15 [OpenAIRE] [PubMed]
Zaki, M, Parthasarathy, S, Ogihara, M, Li, W. New algorithms for fast discovery of association rules. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD). Newport Beach, CA, USA, 1997. : 283-6 [OpenAIRE]
Han, J, Pei, J, Yin, Y. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov. 2004; 8: 53-87
Artamonova, II, Frishman, G, Gelfand, MS. Mining sequence annotation databanks for association patterns. Bioinformatics. 2005; 21: iii49-57 [OpenAIRE] [PubMed]
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