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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 pattern length in mining time.
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Figure 7: (A, B) Impact of pattern length in mining time.

Mentions: The second experiment examined mining time performance according to change in sequence length. Fig. 7A shows the mining time of the surprising contiguous patterns, starting from length-4 to length-9, in a randomly generated DNA sequence database, where we used information gain threshold min_in_gain = 35% and min_conf = 30%. On the other hand, we performed the same experiment with real DNA sequence datasets, where we used the value of min_in_gain = 45% and min_conf = 40%, which is shown in Fig. 7B. From Fig. 7, we can see that our proposed approach could mine the surprising contiguous patterns within a reasonable computation cost.


Efficient mining of interesting patterns in large biological sequences.

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

(A, B) Impact of pattern length in mining time.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: (A, B) Impact of pattern length in mining time.
Mentions: The second experiment examined mining time performance according to change in sequence length. Fig. 7A shows the mining time of the surprising contiguous patterns, starting from length-4 to length-9, in a randomly generated DNA sequence database, where we used information gain threshold min_in_gain = 35% and min_conf = 30%. On the other hand, we performed the same experiment with real DNA sequence datasets, where we used the value of min_in_gain = 45% and min_conf = 40%, which is shown in Fig. 7B. From Fig. 7, we can see that our proposed approach could mine the surprising contiguous patterns within a reasonable computation cost.

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.