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Mining frequent biological sequences based on bitmap without candidate sequence generation

Wang, Qian; Davis, Darryl N.; Ren, Jiadong

Authors

Qian Wang

Jiadong Ren



Abstract

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability.

Journal Article Type Article
Publication Date Feb 1, 2016
Journal Computers in biology and medicine
Print ISSN 0010-4825
Electronic ISSN 1879-0534
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 69
Pages 152-157
APA6 Citation Wang, Q., Davis, D. N., & Ren, J. (2016). Mining frequent biological sequences based on bitmap without candidate sequence generation. Computers in biology and medicine, 69, 152-157. https://doi.org/10.1016/j.compbiomed.2015.12.016
DOI https://doi.org/10.1016/j.compbiomed.2015.12.016
Keywords Biological sequence; Frequent pattern; Bitmap; Quicksort list
Publisher URL http://www.sciencedirect.com/science/article/pii/S0010482515004096
Additional Information Authors' accepted manuscript of an article published in: Computers in biology and medicine, 2016, v.69.

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© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/





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