Novel Adaptive Exon Predictor for DNA Analysis Using Singular Value Decomposition

This article describes how a realistic prediction of the exon regions in deoxyribonucleic acid (DNA) is a key task in the field of genomics. Learning of the protein coding regions is a key aspect of disease identification and designing drugs. These sections of DNA are known as exons, that show three base periodicity (TBP) which serves as a base for all exon locating methods. Many techniques have been applied successfully, but development is still needed in this area. We develop a novel adaptive exon predictor (AEP) using singular value decomposition (SVD) which notably reduces computational complexity and provides better performance in terms of accuracy. Finally, the exon locating capability of proposed SVD based AEP is tested using a real DNA sequence with accession AF099922, obtained from the National Center for Biotechnology Information (NCBI) database and compared with the existing LMS methods. It was shown that proposed AEP is more efficient for locating the exon regions in a DNA sequence.