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FFcPsA: a fast finite conventional state using prefix pattern gene search algorithm for large sequence identification

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Abstract

Gnomic information continues to flood, and this trend comes in the wake of the life sciences’ rapid development. The eventuality has been an increase in the demand for more scalable and faster searching techniques, with the demand also proving urgent. Whereas a faster algorithm could be used to search biomedical data, the process of making gene prediction remains challenging. Particularly, the searching of biomedical data has been affirmed to be a simple gradient base approach. Therefore, indexing has been investigated with the aim of achieving a fast finite conventional rate. With biomedical expressed datasheet at hand, data-based large sequence identification has been achieved via the prefix pattern gene search algorithm. Imperative to note is that real-value expression matrices can replace microarray experimental gene expression data. To ensure that the genomic dataset’s querying exhibits reductions in the overall retrieval time and that the time used for pattern array building is sped up, parallel partitioned methods have gained application. Notably, the central merit accruing from the latter method is that the majority of unrelated sequences are skipped. Also, these methods ensure that the real search problems are only decomposed to establish original database fractions. To ensure that the establishment of the gene’s hidden information and similar characteristics is enhanced, large genetic data patterns are required.

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References

  • Abualigah LM, Khader AT, AI-Betar MA (2016) Unsupervised feature selection technique based on harmony search. In: 2016 7th international conference on computer science and information technology (CSIT), IEEE

  • Agrawal A, Khaitan SK (2008) A new heuristic for multiple sequence alignment. In: IEEE international conference on electro/information technology, pp 215–217

  • Archuleta J, Tilevich E, Feng W (2007) A maintainable software architecture for fast and modular bioinformatics sequence search. In: IEEE international conference on software maintenance

  • Boyer RS, Srother Moore J (1977) A fast string searching algorithm. Commun Assoc Comput Mach 20(10):762–772

    MATH  Google Scholar 

  • Ceri S, Braga D, Corcoglioniti F, Grossniklaus M, Vadacca S (2010) Search computing challenges and directions. Springer, Berlin

    Book  Google Scholar 

  • Chang YF, Chen CY, Chen HW, Lin IH (2005) Bioinformatics analysis for genome design and synthetic biology. In: Proceedings of emerging information technology conference

  • Chao-Xue W et al (2015) An improved gene expression programming algorithm based on hybrid strategy. In: 2015 8th international conference on biomedical engineering and informatics (BMEI), IEEE

  • Chimmanga K, Kalezhi J, Mumba P (2016) Application of best first search algorithm to demand control. In: 2016 IEEE PES power Africa conference, IEEE, pp 51–55

  • Fuyao Z, Qingwei L (2009) A string matching algorithm based on efficient hash function. In: International conference on information engineering and computer science, pp 1–4

  • Gupta V, Singh M, Vinod KB (2014) Pattern matching algorithms for intrusion detection and prevention system: a comparative analysis. In: International conference on advances in computing, communications and informatics, pp 50–54

  • Masseroli M, Picozzi M, Ghisalberti G, Ceri S (2014) Explorative search of distributed bio-data to answer complex biomedical questions. BMC Bioinform 15(Suppl 1):S3

    Article  Google Scholar 

  • Ooi BC, Pang HH, Wang H, Wong L, Yu C (2002) Fast filter-and-refine algorithms for subsequence selection. In: Proceedings of IDEAS, pp 243–255, Research 2001

  • Ozturk O, Ferhatosmanoglu H (2003) Effective indexing and filtering for similarity search in large biosequence databases. In: Proceedings of IEEE symposium on bioinformatics and bioengineering, March 2003, pp 359–366

  • Paira S, Chandra S, Safikul Alam Sk, Patra SS (2014) Bi linear search a new session of searching. IJARCSSE 4(3):459–463

    Google Scholar 

  • Peddapati S, Phanisri Kruthiventi KK (2016) A new random search algorithm: multiple solution vector approach. In: 2016 6th international advanced computing conference, IEEE, pp 187–190

  • Qian G, Zhu Q, Xue Q, Pramanik S (2003) The tree: a dynamic indexing technique for multidimensional non-ordered discrete data spaces. In: Proceedings of VLDB, Germany, Sept 2003

  • Safavi AA, Kelarestaghi M, Eshghi F (2017) Gene expression programming with a local search operator. In: Artificial intelligence and signal processing conference (AISP)

  • Sahinalp SC, Tasan M, Macker J, Ozsoyoglu Z (2003) Distance based indexing for string proximity search. In: Proceedings of ICDE

  • Thorsen O, Jiang K, Peters A, Smith B, Lin H, Feng W, Sosa C (2007) Parallel genomic sequence search on a massively parallel system. In: ACM international conference on computing frontiers, May 2007

  • Wahlström S (2013) Evaluation of string searching algorithms, pp 1–6

  • Xiao S, Lin H, Feng W (2013) Accelerating protein sequence search in a heterogeneous computing system. In: 2011 IEEE international parallel distributed processing symposium, May 2011

  • Xu B, Zhou X, Li J (2006) Recursive shift indexing: a fast multi-pattern string matching algorithm. In: Proceedings of the 4th international conference on applied cryptography and network security (ACNS)

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Authors and Affiliations

  1. Anna University, Chennai, India

    A. Surendar

  2. SENSE, VIT University, Vellore, India

    M. Arun

  3. Department of ECE, KSR College of Engineering, Tiruchengode, India

    A. Mahabub Basha

Authors
  1. A. Surendar
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  2. M. Arun
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  3. A. Mahabub Basha
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Corresponding author

Correspondence to A. Surendar.

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Ethical approval

In situations, where human participants were involved, ethical guidelines stated by the national research committee were followed. Also, the study operated in line with the 1964 Helsinki Declaration regarding ethical guidelines governing the research process.

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Imperatively, all participants were requested to provide permission or informed consent before participating in the study. The informed consent was also secured after clarifying the main aim and specific objectives of the study.

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The first author declares that there is no conflict of interest in this study, and the second author also declares that there is no conflict of interest in the scholarly investigation described above.

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Communicated by P. Pandian.

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Surendar, A., Arun, M. & Mahabub Basha, A. FFcPsA: a fast finite conventional state using prefix pattern gene search algorithm for large sequence identification. Soft Comput 23, 2761–2771 (2019). https://doi.org/10.1007/s00500-018-03733-2

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  • DOI: https://doi.org/10.1007/s00500-018-03733-2

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