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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 min_in_gain.
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Figure 6: Memory usage w.r.t. change of min_in_gain.

Mentions: The memory consumption of our proposed approach and [13] for different values of min_in_gain over a real DNA sequence database is shown in Fig. 6. The x-axis in the graph indicates the change in min_in_gain as a percentage of the data point. A tree with a fixed length-10 was constructed using the aforementioned real datasets, and min_conf value 0.35 is taken. Fig. 6 indicates that for increasing the value of min_in_gain for both approaches, fewer candidates are generated and less memory is required.


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 min_in_gain.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Memory usage w.r.t. change of min_in_gain.
Mentions: The memory consumption of our proposed approach and [13] for different values of min_in_gain over a real DNA sequence database is shown in Fig. 6. The x-axis in the graph indicates the change in min_in_gain as a percentage of the data point. A tree with a fixed length-10 was constructed using the aforementioned real datasets, and min_conf value 0.35 is taken. Fig. 6 indicates that for increasing the value of min_in_gain for both approaches, fewer candidates are generated and less memory is required.

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.