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An efficient approach to mining maximal contiguous frequent patterns from large DNA sequence databases.

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

Bottom Line: The challenge is to find longer sequences without specifying sequence lengths in advance.In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets.The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

View Article: PubMed Central - PubMed

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

ABSTRACT
Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

No MeSH data available.


Related in: MedlinePlus

Retrieval performance w.r.t. change of minimum support (bacteria genome dataset). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.
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Figure 5: Retrieval performance w.r.t. change of minimum support (bacteria genome dataset). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.

Mentions: With various values of minimum support, we compared the run-time performance of three approaches: MCFS (our algorithm), MacosVSpan [8], and Latest Approach [9]. Figs. 4 and 5 show the retrieval performance with respect to the change of minimum support, indicating that MCFS outperforms the other two.


An efficient approach to mining maximal contiguous frequent patterns from large DNA sequence databases.

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

Retrieval performance w.r.t. change of minimum support (bacteria genome dataset). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3475483&req=5

Figure 5: Retrieval performance w.r.t. change of minimum support (bacteria genome dataset). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.
Mentions: With various values of minimum support, we compared the run-time performance of three approaches: MCFS (our algorithm), MacosVSpan [8], and Latest Approach [9]. Figs. 4 and 5 show the retrieval performance with respect to the change of minimum support, indicating that MCFS outperforms the other two.

Bottom Line: The challenge is to find longer sequences without specifying sequence lengths in advance.In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets.The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

View Article: PubMed Central - PubMed

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

ABSTRACT
Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

No MeSH data available.


Related in: MedlinePlus