Genomic Analysis using Higher Order Adaptive Exon Predictors

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.