Mining frequent biological sequences based on bitmap without candidate sequence generation

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

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.

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

• Journal Article Type: Article
• Acceptance Date: Dec 22, 2015
• Online Publication Date: Dec 30, 2015
• Publication Date: Feb 1, 2016
• Deposit Date: Jan 6, 2016
• Publicly Available Date: Nov 23, 2017
• 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
• DOI: https://doi.org/10.1016/j.compbiomed.2015.12.016
• Keywords: Biological sequence; Frequent pattern; Bitmap; Quicksort list
• Public URL: https://hull-repository.worktribe.com/output/383829
• 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.