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Gene Prediction

2019-05-19 Science
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Release : 2019-05-19
Genre : Science
Kind : eBook
Book Rating : 723/5 ( reviews)

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Book Synopsis Gene Prediction by : Martin Kollmar

Download or read book Gene Prediction written by Martin Kollmar. This book was released on 2019-05-19. Available in PDF, EPUB and Kindle. Book excerpt: This volume introduces software used for gene prediction with focus on eukaryotic genomes. The chapters in this book describe software and web server usage as applied in common use-cases, and explain ways to simplify re-annotation of long available genome assemblies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary computational requirements, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Gene Prediction: Methods and Protocols is a valuable resource for researchers and research groups working on the assembly and annotation of single species or small groups of species. Chapter 3 is available open access under a CC BY 4.0 license via link.springer.com.

Methods for Computational Gene Prediction

2007-08-16 Computers
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Release : 2007-08-16
Genre : Computers
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Methods for Computational Gene Prediction by : William H. Majoros

Download or read book Methods for Computational Gene Prediction written by William H. Majoros. This book was released on 2007-08-16. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained, rigorous text describing models used to identify genes in genomic DNA sequences.

Essential Bioinformatics

2006-03-13 Science
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Release : 2006-03-13
Genre : Science
Kind : eBook
Book Rating : 62X/5 ( reviews)

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Book Synopsis Essential Bioinformatics by : Jin Xiong

Download or read book Essential Bioinformatics written by Jin Xiong. This book was released on 2006-03-13. Available in PDF, EPUB and Kindle. Book excerpt: Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.

Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms

2021-06-30 Science
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Release : 2021-06-30
Genre : Science
Kind : eBook
Book Rating : 153/5 ( reviews)

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Book Synopsis Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms by : Md. Zia Ur Rahman

Download or read book Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms written by Md. Zia Ur Rahman. This book was released on 2021-06-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Handbook of Intelligent Computing and Optimization for Sustainable Development

2022-02-11 Technology & Engineering
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Release : 2022-02-11
Genre : Technology & Engineering
Kind : eBook
Book Rating : 622/5 ( reviews)

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Book Synopsis Handbook of Intelligent Computing and Optimization for Sustainable Development by : Mukhdeep Singh Manshahia

Download or read book Handbook of Intelligent Computing and Optimization for Sustainable Development written by Mukhdeep Singh Manshahia. This book was released on 2022-02-11. Available in PDF, EPUB and Kindle. Book excerpt: HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

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