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Efficient mining of interesting patterns in large biological sequences.

Rashid MM, Karim MR, Jeong BS, Choi HJ - Genomics Inform (2012)

Bottom Line: So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not.In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin.Experimental results show that our approach can find interesting patterns within an acceptable computation time.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin 446-701, Korea.

ABSTRACT
Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

No MeSH data available.


(A, B) Impact of information gain threshold on mining time.
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Figure 8: (A, B) Impact of information gain threshold on mining time.

Mentions: The third experiment shows the effect of information gain threshold on mining time to find out the surprising contiguous patterns up to length 8. In this experiment, we take min_conf 0.3 and 0.4 for the random and real datasets, respectively. Fig. 8 indicates that increasing the information gain threshold decreases mining time for both random and real datasets.


Efficient mining of interesting patterns in large biological sequences.

Rashid MM, Karim MR, Jeong BS, Choi HJ - Genomics Inform (2012)

(A, B) Impact of information gain threshold on mining time.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3475482&req=5

Figure 8: (A, B) Impact of information gain threshold on mining time.
Mentions: The third experiment shows the effect of information gain threshold on mining time to find out the surprising contiguous patterns up to length 8. In this experiment, we take min_conf 0.3 and 0.4 for the random and real datasets, respectively. Fig. 8 indicates that increasing the information gain threshold decreases mining time for both random and real datasets.

Bottom Line: So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not.In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin.Experimental results show that our approach can find interesting patterns within an acceptable computation time.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin 446-701, Korea.

ABSTRACT
Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

No MeSH data available.