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.
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Wang, C., Lu, J., Keech, M. (2014). Applications of Concurrent Sequential Patterns in Protein Data Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_19
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DOI: https://doi.org/10.1007/978-3-319-08979-9_19
Publisher Name: Springer, Cham
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