Application of data mining techniques to protein-protein interaction prediction
dc.citation.epage | 323 | en_US |
dc.citation.spage | 316 | en_US |
dc.citation.volumeNumber | 2869 | en_US |
dc.contributor.author | Kocatas, A. | en_US |
dc.contributor.author | Gursoy, A. | en_US |
dc.contributor.author | Atalay, R. | en_US |
dc.date.accessioned | 2016-02-08T10:28:47Z | |
dc.date.available | 2016-02-08T10:28:47Z | |
dc.date.issued | 2003 | en_US |
dc.department | Department of Molecular Biology and Genetics | en_US |
dc.description.abstract | Protein-protein interactions are key to understanding biological processes and disease mechanisms in organisms. There is a vast amount of data on proteins waiting to be explored. In this paper, we describe application of data mining techniques, namely association rule mining and ID3 classification, to the problem of predicting protein-protein interactions. We have combined available interaction data and protein domain decomposition data to infer new interactions. Preliminary results show that our approach helps us find plausible rules to understand biological processes. © Springer-Verlag Berlin Heidelberg 2003. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T10:28:47Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2003 | en |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/11693/24397 | |
dc.language.iso | English | en_US |
dc.publisher | Springer-Verlag Berlin | en_US |
dc.source.title | Lecture Notes in Computer Science | en_US |
dc.title | Application of data mining techniques to protein-protein interaction prediction | en_US |
dc.type | Article | en_US |
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