# Grasping frequent subgraph mining for bioinformatics applications

- Published: 01 Sep 2018
- Publisher: Springer Nature America, Inc
- Country: Belgium

- Link this publication to...
- Cite this publication
Add to ORCID Please grant OpenAIRE to access and update your ORCID works.This research outcome is the result of merged research outcomes in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged research outcome.- add annotation

- University of Antwerp Belgium

Koyutürk, M, Grama, A, Szpankowski, W. An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics. 2004; 20 (suppl 1): 200-7 [OpenAIRE] [DOI]

Hu, H, Yan, X, Huang, Y, Han, J, Zhou, XJ. Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics. 2005; 21 (suppl 1): 213-21 [OpenAIRE] [DOI]

Cakmak, A, Ozsoyoglu, G. Mining biological networks for unknown pathways. Bioinformatics. 2007; 23 (20): 2775-83 [OpenAIRE] [PubMed] [DOI]

Meysman, P, Zhou, C, Cule, B, Goethals, B, Laukens, K. Mining the entire protein databank for frequent spatially cohesive amino acid patterns. BioData Min. 2015; 8 (1): 1 [OpenAIRE] [PubMed] [DOI]

Jiang, C, Coenen, F, Zito, M. A survey of frequent subgraph mining algorithms. Knowl Eng Rev. 2013; 28 (01): 75-105 [DOI]

Han, J, Cheng, H, Xin, D, Yan, X. Frequent pattern mining: current status and future directions. Data Min Knowl Disc. 2007; 15 (1): 55-86 [OpenAIRE] [DOI]

Washio, T, Motoda, H. State of the art of graph-based data mining. SIGKDD Explor Newsl. 2003; 5 (1): 59-68 [OpenAIRE] [DOI]

Fortin, S. The graph isomorphism problem. Technical report, Technical Report 96-20. 1996

9 Inokuchi A, Washio T, Motoda H. An apriori-based algorithm for mining frequent substructures from graph data. In: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery. PKDD ’00: 2000. p. 13–23.

10 Yan X, Han J. gspan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining. ICDM ’02: 2002. p. 721.

11 Zaki MJ. Efficiently mining frequent trees in a forest. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’02: 2002. p. 71–80.

12 Asai T, Abe K, Kawasoe S, Arimura H, Sakamoto H, Arikawa S. Efficient substructure disco very from large semi-structured data. In: Proceedings of the 2002 SIAM International Conference on Data Mining: 2002. p. 158–74.

Pržulj, N, Corneil, DG, Jurisica, I. Efficient estimation of graphlet frequency distributions in protein–protein interaction networks. Bioinformatics. 2006; 22 (8): 974-80 [OpenAIRE] [PubMed] [DOI]

Pržulj, N. Biological network comparison using graphlet degree distribution. Bioinformatics. 2007; 23 (2): 177-83 [OpenAIRE] [PubMed] [DOI]

Hočevar, T, Demšar, J. A combinatorial approach to graphlet counting. Bioinformatics. 2013; 30 (4): 559-65 [OpenAIRE] [PubMed] [DOI]

###### Related research

- University of Antwerp Belgium

Koyutürk, M, Grama, A, Szpankowski, W. An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics. 2004; 20 (suppl 1): 200-7 [OpenAIRE] [DOI]

Hu, H, Yan, X, Huang, Y, Han, J, Zhou, XJ. Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics. 2005; 21 (suppl 1): 213-21 [OpenAIRE] [DOI]

Cakmak, A, Ozsoyoglu, G. Mining biological networks for unknown pathways. Bioinformatics. 2007; 23 (20): 2775-83 [OpenAIRE] [PubMed] [DOI]

Meysman, P, Zhou, C, Cule, B, Goethals, B, Laukens, K. Mining the entire protein databank for frequent spatially cohesive amino acid patterns. BioData Min. 2015; 8 (1): 1 [OpenAIRE] [PubMed] [DOI]

Jiang, C, Coenen, F, Zito, M. A survey of frequent subgraph mining algorithms. Knowl Eng Rev. 2013; 28 (01): 75-105 [DOI]

Han, J, Cheng, H, Xin, D, Yan, X. Frequent pattern mining: current status and future directions. Data Min Knowl Disc. 2007; 15 (1): 55-86 [OpenAIRE] [DOI]

Washio, T, Motoda, H. State of the art of graph-based data mining. SIGKDD Explor Newsl. 2003; 5 (1): 59-68 [OpenAIRE] [DOI]

Fortin, S. The graph isomorphism problem. Technical report, Technical Report 96-20. 1996

9 Inokuchi A, Washio T, Motoda H. An apriori-based algorithm for mining frequent substructures from graph data. In: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery. PKDD ’00: 2000. p. 13–23.

10 Yan X, Han J. gspan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining. ICDM ’02: 2002. p. 721.

11 Zaki MJ. Efficiently mining frequent trees in a forest. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’02: 2002. p. 71–80.

12 Asai T, Abe K, Kawasoe S, Arimura H, Sakamoto H, Arikawa S. Efficient substructure disco very from large semi-structured data. In: Proceedings of the 2002 SIAM International Conference on Data Mining: 2002. p. 158–74.

Pržulj, N, Corneil, DG, Jurisica, I. Efficient estimation of graphlet frequency distributions in protein–protein interaction networks. Bioinformatics. 2006; 22 (8): 974-80 [OpenAIRE] [PubMed] [DOI]

Pržulj, N. Biological network comparison using graphlet degree distribution. Bioinformatics. 2007; 23 (2): 177-83 [OpenAIRE] [PubMed] [DOI]

Hočevar, T, Demšar, J. A combinatorial approach to graphlet counting. Bioinformatics. 2013; 30 (4): 559-65 [OpenAIRE] [PubMed] [DOI]