Genomic Analysis using Higher Order Adaptive Exon Predictors

Authors

  • Srinivasareddy Putluri Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur – 522002 Author
  • Nagesh Mantravadi Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur – 522002 Author
  • Md. Zia Ur Rahman Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur Author

DOI:

https://doi.org/10.61841/f2wqje48

Keywords:

adaptive exon predictor, computational complexity, deoxyribonucleic acid, disease medications, exon, three base periodicity

Abstract

In genomics, true identifying exon regions in deoxyribonucleic acid (DNA) sections are an important activity for the identification and development of disease medications. All exon identification techniques are based on three basic periodicity (TBP) properties of exons. The techniques of adaptive sign processing have been successful compared to various other methods. This paper uses the least mean fourth (LMF) algorithm also its signed variants that includes SRLMF, SLMF also SSLMF algorithms to develop multiple adaptive exon predictors (AEPs) with less computational complexity. Eventually, a performance evaluation is performed for different AEPs using various standard gene data sequences derived from National Biotechnology Information Centre (NBI) genomic sequence database, such as Sensitivity (Sn), Precision (Pr) and Specificity (Sp) measurements.

 

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Published

31.10.2020

Issue

Vol. 24 No. 8 (2020): 24 (8) 2020

Section

Articles

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How to Cite

Putluri, S., Mantravadi, N., & Rahman, M. Z. U. (2020). Genomic Analysis using Higher Order Adaptive Exon Predictors. International Journal of Psychosocial Rehabilitation, 24(8), 7859-7867. https://doi.org/10.61841/f2wqje48