Limits...
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

Performance of MCFS algorithm w.r.t. increasing minimum support in partitioning approach (on Homo sapiens GRCh37.64 DNA Chromosome Part 1, 2, 3). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Performance of MCFS algorithm w.r.t. increasing minimum support in partitioning approach (on Homo sapiens GRCh37.64 DNA Chromosome Part 1, 2, 3). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.

Mentions: Finally, we validate our combined memory disk-based approach by applying it to Homo sapiens GRCh37.64 DNA Chromosome Parts 1, 2, and 3. We assume that Parts 1, 2, and 3 are partitioned and stored on the disk. With various settings of minimum support threshold, we measured the run-time performance (Fig. 8).


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)

Performance of MCFS algorithm w.r.t. increasing minimum support in partitioning approach (on Homo sapiens GRCh37.64 DNA Chromosome Part 1, 2, 3). 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 8: Performance of MCFS algorithm w.r.t. increasing minimum support in partitioning approach (on Homo sapiens GRCh37.64 DNA Chromosome Part 1, 2, 3). MCFS, Maximal Contiguous Frequent Suffix tree algorithm.
Mentions: Finally, we validate our combined memory disk-based approach by applying it to Homo sapiens GRCh37.64 DNA Chromosome Parts 1, 2, and 3. We assume that Parts 1, 2, and 3 are partitioned and stored on the disk. With various settings of minimum support threshold, we measured the run-time performance (Fig. 8).

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