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Comput Biol Med. 2016 Feb 1;69:152-7. doi: 10.1016/j.compbiomed.2015.12.016. Epub 2015 Dec 30.

Mining frequent biological sequences based on bitmap without candidate sequence generation.

Author information

1
College of Information Science and Engineering, Yanshan University, Qianhuangdao, Hebei, China; Computer Virtual Technology and System Integration Laboratory of Hebei Province, China; Department of Computer Science, University of Hull, Hull, UK. Electronic address: wanqianysu@163.com.
2
Department of Computer Science, University of Hull, Hull, UK.
3
College of Information Science and Engineering, Yanshan University, Qianhuangdao, Hebei, China; Computer Virtual Technology and System Integration Laboratory of Hebei Province, China.

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.

KEYWORDS:

Biological sequence; Bitmap; Frequent pattern; Quicksort list

[Indexed for MEDLINE]

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