MpBsmi: A new algorithm for the recognition of continuous biological sequence pattern based on index structure
- Published
- Accepted
- Subject Areas
- Computational Biology, Data Mining and Machine Learning
- Keywords
- sequence position table, sequence database index, biological sequence, continuous sequence pattern, sequence pattern mining
- Copyright
- © 2018 Li et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. MpBsmi: A new algorithm for the recognition of continuous biological sequence pattern based on index structure. PeerJ Preprints 6:e26471v1 https://doi.org/10.7287/peerj.preprints.26471v1
Abstract
A significant approach for the discovery of biological regulatory rules of genes, protein and their inheritance relationships is the extraction of meaningful patterns from biological sequence data.The existing algorithms of sequence pattern discovery, like MSPM and FBSB, suffice their low efficiency and accuracy. In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data Index Structure.The MpBsmi algorithm employs a sequence position table abbreviated ST and a sequence database index structure named DB-Index for data storing, mining and pattern expansion. The ST and DB-Index of single items are firstly obtained through scanning sequence database once. Then a new algorithm for fast support counting is developed to mine the table ST to identify the frequent single items. Based on a recursive connection strategy, the frequenct patterns are expanded and the expanded table ST is updated by scanning the DB-Index. The fast support counting algorithm is used for obtaining the frequent expansion patterns. Finally, a new pruning techniqueis developed for extended pattern to avoid the generation of unnecessarily large number of candidate patterns. The experiments results on multiple the classical protein sequence from the Pfam database validate the performance of the proposed algorithm including the accuracy, stability and scalability. It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining.
Author Comment
This is a submission to PeerJ for review.