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E-raamat: Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms [Taylor & Francis e-raamat]

(KLEF, India),
  • Formaat: 192 pages, 37 Tables, black and white; 81 Line drawings, black and white; 4 Halftones, black and white; 85 Illustrations, black and white
  • Ilmumisaeg: 30-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9780367618551
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  • Taylor & Francis e-raamat
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Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing AlgorithmsSuurem pilt
  • Formaat: 192 pages, 37 Tables, black and white; 81 Line drawings, black and white; 4 Halftones, black and white; 85 Illustrations, black and white
  • Ilmumisaeg: 30-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9780367618551
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780367618551
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.
Authors ix
Chapter 1 Introduction 1(18)
1.1 Genomics Engineering
1(1)
1.2 DNA Sequence Structure
1(2)
1.3 Motivation for the Work
3(1)
1.4 Objectives
3(1)
1.5 Molecular Basis for Genomic Information
4(3)
1.5.1 Understanding the Genome
4(1)
1.5.2 Building Blocks of DNA
4(3)
1.6 Gene Prediction
7(1)
1.6.1 Significance of Gene Prediction
7(1)
1.7 Types of Gene Prediction Approaches
7(2)
1.7.1 Extrinsic Gene Prediction
7(1)
1.7.2 Ab Initio Gene Prediction
7(2)
1.7.3 Comparative Gene Prediction
9(1)
1.8 DNA Representations for Genomic Sequence Analysis
9(1)
1.8.1 Desirable Properties
9(1)
1.9 Types of DNA Representations
9(6)
1.9.1 Voss Mapping
9(1)
1.9.2 Z-Curve Representation
10(1)
1.9.3 Tetrahedron
11(1)
1.9.4 Complex
12(1)
1.9.5 Quaternion
13(1)
1.9.6 Electron-Ion Interaction Potential
14(1)
1.9.7 Inter-nucleotide Distance
14(1)
1.9.8 Maximum Likelihood Estimate
14(1)
1.10 Organization of Book
15(4)
Chapter 2 Literature Review 19(12)
2.1 Biological Background of Genomic Sequence Analysis
19(1)
2.2 The Gene and Early Development of Genetics
19(3)
2.3 Origin of Three-Base Periodicities in Genomic Sequences
22(1)
2.4 DSP-Based Techniques for DNA Analysis
23(6)
2.4.1 Application of Discrete Fourier Transform
24(1)
2.4.2 Spectral Content (SC) Measure
25(1)
2.4.3 Optimized Spectral Content (SC) Measure
26(1)
2.4.4 Spectral Rotation (SR) Measure
26(1)
2.4.5 Fourier Product Spectrum (FPS) Method
27(1)
2.4.6 Digital Filters for Genomic Analysis
27(2)
2.4.7 Autoregressive Models
29(1)
2.5 Adaptive Algorithms for DNA Analysis
29(1)
2.6 Conclusions
30(1)
Chapter 3 Sign LMS Based Realization of Adaptive Filtering Techniques for Exon Prediction 31(66)
3.1 Introduction
31(1)
3.2 Theoretical Considerations of Adaptive Filtering Techniques in DNA Analysis
32(3)
3.2.1 Adaptive Filter
32(1)
3.2.2 Properties of Adaptive Algorithms
32(3)
3.2.3 Need for Development of Adaptive Exon Predictors
35(1)
3.3 Structure of Adaptive Exon Predictor for DNA Analysis
35(1)
3.4 LMS Algorithm
36(3)
3.5 LMF Algorithm
39(4)
3.6 Variable Step Size LMS (VSLMS) Algorithm
43(2)
3.7 Least Mean Logarithmic Squares (LMLS) Algorithm
45(3)
3.8 Least Logarithmic Absolute Difference (LLAD) Algorithm
48(4)
3.9 Simplified Algorithms Based on Signum Function
52(2)
3.9.1 Sign-Based LMS Algorithms
52(2)
3.10 Extension to Sign-Based Realizations of LMS-Based Variants
54(2)
3.10.1 Sign-Based Least Mean Fourth (LMF) Algorithms
54(1)
3.10.2 Sign-Based Variable Step Size LMS (VSLMS) Algorithms
55(1)
3.10.3 Sign-Based Least Mean Logarithmic Squares (LMLS) Algorithms
55(1)
3.10.4 Sign-Based Least Logarithmic Absolute Difference (LLAD) Algorithms
55(1)
3.11 Computational Complexity Issues
56(2)
3.12 Convergence Analysis
58(4)
3.13 Results and Discussion for LMS-Based Variants
62(32)
3.13.1 Gene Datasets from the NCBI Gene Databank for Gene Sequence Analysis
64(1)
3.13.2 Analysis of Gene Datasets of NCBI Gene Databank
65(19)
3.13.2.1 Nucleotide Densities of Monomers and Dimers in Gene Dataset
65(19)
3.13.3 Performance Measures of Exon Prediction
84(1)
3.13.4 Exon Prediction Results
85(9)
3.14 Conclusions
94(3)
Chapter 4 Normalization-Based Realization of Adaptive Filtering Techniques for Exon Prediction 97(38)
4.1 Introduction
97(3)
4.2 Normalized Adaptive Algorithms
100(1)
4.3 Normalized LMS (NLMS) Algorithm
100(3)
4.4 Error-Normalized LMS (ENLMS) Algorithm
103(4)
4.5 Normalized Least Mean Fourth (NLMF) Algorithm
107(3)
4.6 Variable Step Size Normalized LMS (VNLMS) Algorithm
110(4)
4.7 Extension to Sign-Based Realizations of Normalized Algorithms
114(4)
4.7.1 Sign-Based Normalized LMS (NLMS) Algorithms
115(1)
4.7.2 Sign-Based Error-Normalized LMS (ENLMS) Algorithms
115(1)
4.7.3 Sign-Based Normalized LMF (NLMF) Algorithms
116(1)
4.7.4 Sign-Based Variable Step Size NLMS (VNLMS) Algorithms
117(1)
4.8 Computational Complexity Issues
118(2)
4.9 Convergence Analysis
120(4)
4.10 Results and Discussion for Normalization-Based Variants
124(9)
4.10.1 Exon Prediction Results
124(9)
4.11 Conclusions
133(2)
Chapter 5 Logarithmic-Based Realization of Adaptive Filtering Techniques for Exon Prediction 135(40)
5.1 Introduction
135(1)
5.2 Logarithmic Adaptive Algorithms
136(3)
5.3 Normalized LMLS (NLMLS) Algorithm
139(4)
5.4 Error-Normalized LMLS (ENLMLS) Algorithm
143(3)
5.5 Normalized LLAD (NLLAD) Algorithm
146(4)
5.6 Error-Normalized LLAD (ENLLAD) Algorithm
150(3)
5.7 Extension to Sign-Based Realizations of Logarithmic Normalized Algorithms
153(4)
5.7.1 Extension to Sign-Based Realizations of NLMLS-Based Variants
153(1)
5.7.2 Extension to Sign-Based Realizations of ENLMLS-Based Variants
154(1)
5.7.3 Extension to Sign-Based Realizations of NLLAD-Based Variants
155(1)
5.7.4 Extension to Sign-Based Realizations of ENLLAD-Based Variants
156(1)
5.8 Computational Complexity Issues
157(2)
5.9 Convergence Analysis
159(1)
5.10 Results and Discussion for Logarithmic Normalized Variants
160(12)
5.10.1 Exon Prediction Results
163(9)
5.11 Conclusions
172(3)
Chapter 6 Conclusion and Future Perspective 175(6)
6.1 Summary and Conclusions
175(3)
6.2 Recommendations for Future Research
178(3)
References 181(8)
Index 189
Prof. Md Zia Ur Rahman is a Professor with the Department of Electronics and Communication Engineering, K. L. University, Koneru Lakshmaiah Educational Foundation Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, medical imaging, array signal processing, MEMS, Nano photonics.

Srinivasareddy Putluri, M.Tech., Ph.D is with the Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational Foundation, K. L. University, Vaddeswaram, Guntur, India. His research interests include genomic signal processing and adaptive signal processing.
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