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 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
Covers various theoretical considerations of adaptive filtering
techniques used for DNA analysis, introduction to adaptive
filtering, properties of adaptive algorithms, need for development
of adaptive exon predictors (AEPs), and structure of AEP used for
DNA analysis Extends the approach of LMS algorithm and its
sign-based realizations with normalization factor for DNA analysis
Presents the normalized logarithmic-based realizations of LMLS and
LLAD adaptive algorithms that include normalized LMLS (NLMLS)
algorithm, normalized LLAD (NLLAD) algorithm, and their signed
variants Provides an overview of the goals achieved and highlights
the primary achievements using all proposed techniques Dr. Md. Zia
Ur Rahman is a professor in the Department of Electronics and
Communication Engineering at Koneru Lakshmaiah Educational
Foundation (K. L. University), Guntur, India. His current research
interests include adaptive signal processing, biomedical signal
processing, genetic engineering, medical imaging, array signal
processing, medical telemetry, and nanophotonics. Dr.
Srinivasareddy Putluri is currently a Software Engineer at Tata
Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree
(Genomic Signal Processing using Adaptive Signal Processing
algorithms) from the Department of Electronics and Communication
Engineering at Koneru Lakshmaiah Educational Foundation (K. L.
University), Guntur, India. His research interests include genomic
signal processing and adaptive signal processing. He has published
15 research papers in various journals and proceedings. He is
currently a reviewer of publishers like the IEEE Access and IGI.
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