Advertisement

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

  • A. SurendarEmail author
  • M. Arun
  • A. Mahabub Basha
Focus
  • 5 Downloads

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.

Keywords

Gene search Pattern extraction Hash function Microarray Gene expression Parallel genetic algorithm 

Notes

Compliance with ethical standards

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.

Informed consent

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.

Conflict of interest

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.

References

  1. 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), IEEEGoogle Scholar
  2. Agrawal A, Khaitan SK (2008) A new heuristic for multiple sequence alignment. In: IEEE international conference on electro/information technology, pp 215–217Google Scholar
  3. Archuleta J, Tilevich E, Feng W (2007) A maintainable software architecture for fast and modular bioinformatics sequence search. In: IEEE international conference on software maintenanceGoogle Scholar
  4. Boyer RS, Srother Moore J (1977) A fast string searching algorithm. Commun Assoc Comput Mach 20(10):762–772zbMATHGoogle Scholar
  5. Ceri S, Braga D, Corcoglioniti F, Grossniklaus M, Vadacca S (2010) Search computing challenges and directions. Springer, BerlinCrossRefGoogle Scholar
  6. Chang YF, Chen CY, Chen HW, Lin IH (2005) Bioinformatics analysis for genome design and synthetic biology. In: Proceedings of emerging information technology conferenceGoogle Scholar
  7. 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), IEEEGoogle Scholar
  8. 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 51–55Google Scholar
  9. Fuyao Z, Qingwei L (2009) A string matching algorithm based on efficient hash function. In: International conference on information engineering and computer science, pp 1–4Google Scholar
  10. 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 50–54Google Scholar
  11. 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):S3CrossRefGoogle Scholar
  12. 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 2001Google Scholar
  13. 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 359–366Google Scholar
  14. Paira S, Chandra S, Safikul Alam Sk, Patra SS (2014) Bi linear search a new session of searching. IJARCSSE 4(3):459–463Google Scholar
  15. Peddapati S, Phanisri Kruthiventi KK (2016) A new random search algorithm: multiple solution vector approach. In: 2016 6th international advanced computing conference, IEEE, pp 187–190Google Scholar
  16. 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 2003Google Scholar
  17. Safavi AA, Kelarestaghi M, Eshghi F (2017) Gene expression programming with a local search operator. In: Artificial intelligence and signal processing conference (AISP)Google Scholar
  18. Sahinalp SC, Tasan M, Macker J, Ozsoyoglu Z (2003) Distance based indexing for string proximity search. In: Proceedings of ICDEGoogle Scholar
  19. 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 2007Google Scholar
  20. Wahlström S (2013) Evaluation of string searching algorithms, pp 1–6Google Scholar
  21. 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 2011Google Scholar
  22. 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)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia
  2. 2.SENSEVIT UniversityVelloreIndia
  3. 3.Department of ECEKSR College of EngineeringTiruchengodeIndia

Personalised recommendations