Protein data modelling for concurrent sequential patterns

Jing Lu, Malcolm Keech, Cuiqing Wang

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review

Abstract

Protein sequences from the same family typically share common patterns which imply their structural function and biological relationship. The challenge of identifying protein motifs is often addressed through mining frequent itemsets and sequential patterns, where post-processing is a useful technique. Earlier work has shown that Concurrent Sequential Patterns mining can be applied in bioinformatics, e.g. to detect frequently occurring concurrent protein sub-sequences. This paper presents a companion approach to data modelling and visualisation, applying it to real-world protein datasets from the PROSITE and NCBI databases. The results show the potential for graph-based modelling in representing the integration of higher level patterns common to all or nearly all of the protein sequences.
Original languageEnglish
Title of host publication5th International Workshop on Biological Knowledge Discovery and Data Mining, 1-4 September 2014, Munich
Pages5-9
Number of pages5
Publication statusPublished - 1 Oct 2014
Externally publishedYes

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