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Applications of concurrent sequential patterns in protein data mining

Wang, Cuiqing and Lu, Jing and Keech, Malcolm. (2014). Applications of concurrent sequential patterns in protein data mining. In: 10th International Conference on Machine Learning and Data Mining, 22-24 July 2014, St. Petersburg.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: TECHNOLOGY > Computing
Faculties: Maritime and Technology Faculty > Faculty of Maritime and Technology (other)
Depositing User: Jing Lu
Date Deposited: 10 Nov 2014 13:38
Last Modified: 18 Nov 2014 11:25
URI: https://ssudl.solent.ac.uk/id/eprint/3046

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