Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants

 
 
 
  • Abstract
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  • Abstract


    In the field of Bio-informatics, locating the exon fragments in a deoxyribonucleic acid (DNA) sequence is an important and vital work. Study of protein coding regions is a wide phenomenon in identification of diseases and design of drugs. The regions of DNA that have the protein coding information are termed as exons. Hence identifying the exon segments in a genomic sequence is a crucial job in bio-informatics. Three base periodicity (TBP) has been observed in the regions of DNA sequences can be easily determined by applying signal processing methods. Adaptive signal processing techniques found to be useful than other available methods. This is due to their unique capability to alter weight coefficients based on genomic sequence. We propose efficient adaptive exon predictors (AEPs) based on these considerations using Proportionate Normalized LMS (PNLMS) algorithm and Maximum Proportionate Normalized LMS (MPNLMS) algorithm to improve exon locating ability and better convergence. To ease the complexity of computations in the denominator during filtering process, proposed AEPs using PNLMS and its maximum variants are combined with signature algorithms. Hybrid variants of proposed AEPs include PNLMS, DCPNLMS, ECPNLMS, SSPNLMS, MPNLMS, MDCPNLMS, MECPNLMS and MSSPNLMS algorithms. It was shown that the AEP based on MDCPNLMS is superior in applications of exon identification depending on performance measures with Sensitivity 0.7346, Specificity 0.7483 and precision 0.7325 for a genomic sequence with accession AF009962 at a threshold of 0.8. Finally the capability of several AEPs in predicting exon locations is verified using different DNA sequences found in National Center for Biotechnology Information (NCBI) gene database.

     

     

  • Keywords


    adaptive exon predictor; bioinformatics; computational complexity; deoxyribonucleic acid; three base periodicity; sensitivity; specificity; precision.

  • References


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Article ID: 11721
 
DOI: 10.14419/ijet.v7i2.17.11721




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