Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms

This book discusses the use of adaptive techniques for improving the accuracy of exon prediction in DNA sequences,which is important for disease diagnosis and therapy. It covers theoretical considerations of adaptive filtering techniques,extends the approach of least mean squares (LMS) algorithm,and presents normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms. The book is intended for undergraduate and postgraduate students,Ph.D. students,and researchers in genetic engineering,biomedical engineering,and bioinformatics.

\n Format: Hardback
\n Length: 192 pages
\n Publication date: 31 May 2021
\n Publisher: Taylor & Francis Ltd
\n

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 achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering,biomedical engineering,and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work.

Features

Presents an comprehensive overview of genomics engineering,the structure of DNA sequences,and their building blocks,along with a detailed explanation of genetic information flow within a cell,gene prediction,and its significance in disease diagnosis and therapy.

Provides a detailed review of literature on genomic sequence analysis,starting with the biological background and fundamentals.

Introduces adaptive filtering techniques,explaining their theoretical considerations and the need for developing adaptive exon predictors (AEPs) for DNA analysis.

Extends the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis.

Presents the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms,including their signed variants.

Outlines the goals achieved and highlights the primary achievements using all proposed techniques.

Designed for undergraduate and postgraduate students,as well as Ph.D. students and researchers,this book serves as a valuable resource for rigorous application of adaptive signal processing algorithms in genetic engineering,biomedical engineering,and bioinformatics.

Provides practical guidance for Ph.D. students and researchers,as well as offering numerous research directions for further work in this field.

\n Weight: 450g\n
Dimension: 162 x 241 x 19 (mm)\n
ISBN-13: 9780367615802\n \n