Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms [Paperback]

1. Introduction.  2. Review of Literature.  3. Sign LMS Based Realization of Adaptive Filtering Techniques for Exon Prediction.  4. Normalization Based Realization of Adaptive Filtering Techniques for Exon Prediction.  5. Logarithmic Based Realization of Adaptive Filtering Techniques for Exon Prediction.  6. Conclusions and Future Perspective

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achievlÄ