Adaptive Exon Prediction using Maximum Modified Normalized Algorithms
Md. Zia Ur Rahman1, Farmanulla Shaik2, Srinivasareddy Putluri3
1Md Zia Ur Rahman, Professor Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational Foundation Guntur, India.
2Farmanullah Shaik, Assistant Professor Department of Electronics and Communications Engineering, Eswar College of Engineering, Kesanupalli, Narasaraopeta, Guntur, A.P., India.
3Srinivasareddy Putluri Department of Electronics and Communication Engineering, K L University, Guntur, A.P., India.
Manuscript received on 09 April 2019 | Revised Manuscript received on 14 May 2019 | Manuscript published on 30 May 2019 | PP: 1662-1666 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2393037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Exact identification of exon fragments in a deoxyribonucleic acid (DNA) sequence is a critical task in the field of genomics. This is a crucial part in finding health disorders and design drugs. Exons are the infoessentialin coding of proteins in DNA. Hence for wardfindingsuch DNA sectionsremains important part of genomics. In DNA arrangement, nucleotides form the key elementary units. Three base periodicity (TBP) is a basic property displayed by only exon fragments, and is not shown in other DNA sections that could beforecastedeasily with techniques of signal processing. Frommany methods, adaptive methodswerefavorablebecause of theircompetencein altering weight coefficients depending on gene sequence. Hence, an adaptive exon predictor (AEP) is proposed with Maximum Modified Normalized Least Mean Square (MMNLMS) algorithm. TheAEP derived using MMNLMS is combined with its sign versions to decrease complexity in computations. Also, this was clear thatModified Normalized Sign Regressor LMS (MMNSRLMS) based AEPwas more effective in exon identification applications withmetricsalikeSpecificity, Sensitivity, and Precision. Thus, computational complexity is greatly minimized, and AEPs proposedweresuitablefor use in nano devices. Lastly exon findingcapabilitywithdiverse AEPs stands verified with DNA datasets from National Center for Biotechnology Information (NCBI) gene databank.
Index Terms: Adaptive Exon Predictor, Disorders, Deoxyribonucleic Acid, exon Fragments, Three Base Periodicity
Scope of the Article: Algorithm Engineering