PMBC: Pattern mining from biological sequences with wildcard constraints

Wu, Xindong; Zhu, Xingquan; He, Yu; Arslan, Abdullah N.

doi:10.1016/j.compbiomed.2013.02.006

Cited by 42 publications

(17 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the OOC problem, there does not yet exist any polynomial algorithm that can achieve a complete solution in a general case, and therefore, it is assumed to be NP-hard [24,29] .…”

Section: Discussionmentioning

confidence: 99%

“…This problem has also been extended to approximate matc- [11][12][13][14] One text and one pattern Fixed wildcards Occurrences Literature [16][17][18][19][20] One text and one pattern Flexible wildcards Ending positions of occurrences Literature [22] One text and multi-patterns Unbounded wildcards Whether one of the patterns occurs in the text Literature [27][28] One text and one pattern Flexible wildcards Number of occurrences Literature [29][30] One text and one pattern Flexible wildcards Occurrences with the one-off condition Literature [23] One text and one pattern Unbounded wildcards Occurrences with the non-overlapping condition Literature [24] One text and one pattern Unbounded wildcards Occurrences with the one-off condition Literature [21] One text and one pattern Unbounded wildcards, Ending positions of occurrences flexible wildcards hing [44][45] and pattern mining [4,46] , which are outside the scope of this paper.…”

Section: Pattern Matching With Wildcardsmentioning

confidence: 99%

“…However, if wildcards are added into the process of matching, this PMW problem becomes much complicated. There are three types of research efforts on dealing with pattern matching with wildcards: 1) a fixed number of wildcards [11][12][13][14][15] , 2) the number range of wildcards to be specified separately [16][17][18][19][20][21] , namely flexible wildcards, and 3) an unbounded number of wildcards [5,[22][23][24] . As the first two types have limited flexibilities for the user's queries, pattern matching with flexible wildcards (PMFW) has drawn more attention.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pattern Matching with Flexible Wildcards

Qiang

Xie

2014

J. Comput. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Pattern matching with wildcards (PMW) has great theoretical and practical significance in bioinformatics, information retrieval, and pattern mining. Due to the uncertainty of wildcards, not only is the number of all matches exponential with respect to the maximal gap flexibility and the pattern length, but the matching positions in PMW are also hard to choose. The objective to count the maximal number of matches one by one is computationally infeasible. Therefore, rather than solving the generic PMW problem, many research efforts have further defined new problems within PMW according to different application backgrounds. To break through the limitations of either fixing the number or allowing an unbounded number of wildcards, pattern matching with flexible wildcards (PMFW) allows the users to control the ranges of wildcards. In this paper, we provide a survey on the state-of-the-art algorithms for PMFW, with detailed analyses and comparisons, and discuss challenges and opportunities in PMFW research and applications.

show abstract

“…For the OOC problem, there does not yet exist any polynomial algorithm that can achieve a complete solution in a general case, and therefore, it is assumed to be NP-hard [24,29] .…”

Section: Discussionmentioning

confidence: 99%

Section: Pattern Matching With Wildcardsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pattern Matching with Flexible Wildcards

Qiang

Xie

2014

J. Comput. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The problem has applications in sequential pattern analysis in data mining [17,20,25], in discovering patterns from biological sequences [24], and in finding repeating patterns in DNA [8]. The pattern matching with wildcards and length constraints problem can be stated as the following: Problem 1 ( [5]).…”

Section: Introductionmentioning

confidence: 99%

New algorithms for pattern matching with wildcards and length constraints

Arslan

George

Stor

2015

Discrete Math. Algorithm. Appl.

Self Cite

View full text Add to dashboard Cite

The pattern matching with wildcards and length constraints problem is an interesting problem in the literature whose computational complexity is still open. There are polynomial time exact algorithms for its special cases. There are heuristic algorithms, and online algorithms that do not guarantee an optimal solution to the original problem. We consider two special cases of the problem for which we develop offline solutions. We give an algorithm for one case with provably better worst case time complexity compared to existing algorithms. We present the first exact algorithm for the second case. This algorithm uses integer linear programming (ILP) and it takes polynomial time under certain conditions.

show abstract

“…Also, the acknowledgment of promoters in DNA Sequences Utilizing Weightily Averaged Onedependence Estimators are clarified by ZawHtike and Shoon Lei Win [25]. To reduce the time complexity and to raise the efficiency of the system, an entropy-based feature extraction approach used to choose related nucleotides that are liable for promoter recognition.…”

Section: Literature Surveymentioning

confidence: 99%

FDSMO: Frequent DNA Sequence Mining Using FBSB and Optimization

Lakshmanna¹,

Khare

2016

IJIES

View full text Add to dashboard Cite

DNA Sequence mining helps in discovering the patterns which can occur frequently, structures of DNA in DNA data sets. Frequent pattern mining is a central strategy for affiliation guideline discovery, but existing calculations experience the ill effects of low effectiveness or poor error rate on the grounds that natural groupings vary from general successions with more attributes. In our last work, we proposed Prefix Span with Group Search Optimization (PSGSO) to optimize the mined results from the Prefix Span method. We propose a new method called Frequent DNA Sequence Mining using Optimization (FDSMO) which combines Frequent Biological Sequence based on Bitmap (FBSB) and Hybrid of Firefly and Group Search Optimization (HFGSO) in this paper. The FDSMO process includes three stages: (i) applying the Frequent Biological Sequence based on Bitmap (ii) calculate length, width and regular expression (iii) optimization using HFGSO. The exploratory results demonstrate that FDSMO performs better than the existing methods, both in terms of running time and scalability.

show abstract

PMBC: Pattern mining from biological sequences with wildcard constraints

Cited by 42 publications

References 35 publications

Pattern Matching with Flexible Wildcards

Pattern Matching with Flexible Wildcards

New algorithms for pattern matching with wildcards and length constraints

FDSMO: Frequent DNA Sequence Mining Using FBSB and Optimization

Contact Info

Product

Resources

About