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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.


Memory usage w.r.t. change of sequence length.
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Figure 5: Memory usage w.r.t. change of sequence length.

Mentions: In the first experiment, we compare the memory usage of our approach and Zerin et al. [13] by varying the sequence length. Fig. 5 shows the memory usage according to the sequence length change, where we used our generated database for construction of the spanning tree with a fixed length 7. Here, we used information gain threshold, min_in_gain = 35(%) and minimum confidence threshold, min_conf = 30(%). From this experiment, we can see that the proposed approach has efficient improvement over Zerin et al. [13]. When the sequence length becomes longer, it shows better performance in comparison with the existing algorithm.


Efficient mining of interesting patterns in large biological sequences.

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

Memory usage w.r.t. change of sequence length.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Memory usage w.r.t. change of sequence length.
Mentions: In the first experiment, we compare the memory usage of our approach and Zerin et al. [13] by varying the sequence length. Fig. 5 shows the memory usage according to the sequence length change, where we used our generated database for construction of the spanning tree with a fixed length 7. Here, we used information gain threshold, min_in_gain = 35(%) and minimum confidence threshold, min_conf = 30(%). From this experiment, we can see that the proposed approach has efficient improvement over Zerin et al. [13]. When the sequence length becomes longer, it shows better performance in comparison with the existing algorithm.

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.