2.50
Hdl Handle:
http://hdl.handle.net/10547/333888
Title:
Applications of concurrent sequential patterns in protein data mining
Authors:
Wang, Cuiqing; Keech, Malcolm; Lu, Jing
Abstract:
Protein sequences of the same family typically share common patterns which imply their structural function and biological relationship. Traditional sequential patterns mining has its focus on mining frequently occurring sub-sequences. However, a number of applications motivate the search for more structured patterns, such as protein motif mining. This paper builds on the original idea of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach in bioinformatics. Specifically, a new method and algorithms are presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Experiments with real-world protein datasets highlight the applicability of the ConSP methodology in protein data mining. The results show the potential for knowledge discovery in the field of protein structure identification.
Affiliation:
University of Bedfordshire
Citation:
Wang, C., Keech, M., Lu, J. (2014) 'Applications of concurrent sequential patterns in protein data mining'. 10th International Conference on Machine Learning and Data Mining, St. Petersburg, Russia, 21st - 24th July. Available at: http://link.springer.com/chapter/10.1007%2F978-3-319-08979-9_19
Publisher:
Springer
Journal:
Lecture Notes in Computer Science
Issue Date:
2014
URI:
http://hdl.handle.net/10547/333888
DOI:
10.1007/978-3-319-08979-9_19
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-319-08979-9_19
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9783319089782
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Cuiqingen
dc.contributor.authorKeech, Malcolmen
dc.contributor.authorLu, Jingen
dc.date.accessioned2014-11-10T13:29:13Z-
dc.date.available2014-11-10T13:29:13Z-
dc.date.issued2014-
dc.identifier.citationWang, C., Keech, M., Lu, J. (2014) 'Applications of concurrent sequential patterns in protein data mining'. 10th International Conference on Machine Learning and Data Mining, St. Petersburg, Russia, 21st - 24th July. Available at: http://link.springer.com/chapter/10.1007%2F978-3-319-08979-9_19en
dc.identifier.isbn9783319089782-
dc.identifier.doi10.1007/978-3-319-08979-9_19-
dc.identifier.urihttp://hdl.handle.net/10547/333888-
dc.description.abstractProtein sequences of the same family typically share common patterns which imply their structural function and biological relationship. Traditional sequential patterns mining has its focus on mining frequently occurring sub-sequences. However, a number of applications motivate the search for more structured patterns, such as protein motif mining. This paper builds on the original idea of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach in bioinformatics. Specifically, a new method and algorithms are presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Experiments with real-world protein datasets highlight the applicability of the ConSP methodology in protein data mining. The results show the potential for knowledge discovery in the field of protein structure identification.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-08979-9_19en
dc.subjectprotein sequencesen
dc.subjectdata miningen
dc.subjectconcurrent sequential patterns (ConSP)en
dc.subjectbioinformaticsen
dc.subjectConSP miningen
dc.subjectconcurrent vector methoden
dc.subjectPROSITEen
dc.subjectknowledge discoveryen
dc.titleApplications of concurrent sequential patterns in protein data miningen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalLecture Notes in Computer Scienceen
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