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Multiple Biological Sequence Alignment - Scoring Functions, Algorithms And Evaluation

by Ken Nguyen, Xuan Guo e Yi Pan

125,81€ + 10% CARD

 Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search. This book contains 11 chapters, with Chapter 1 providing basic information on biological sequences. Next, Chapter 2 contains fundamentals in pair-wise sequence alignment, while Chapters 3 and 4 examine popular existing quantitative models and practical clustering techniques that have been used in multiple sequence alignment. Chapter 5 describes, characterizes and relates many multiple sequence alignment models. Chapter 6 describes how traditionally phylogenetic trees have been constructed, and available sequence knowledge bases can be used to improve the accuracy of reconstructing phylogeny trees. Chapter 7 covers the latest methods developed to improve the run-time efficiency of multiple sequence alignment. Next, Chapter 8 covers several popular existing multiple sequence alignment server and services, and Chapter 9 examines several multiple sequence alignment techniques that have been developed to handle short sequences (reads) produced by the Next Generation Sequencing technique (NSG). Chapter 10 describes a Bioinformatics application using multiple sequence alignment of short reads or whole genomes as input. Lastly, Chapter 11 provides a review of RNA and protein secondary structure prediction using the evolution information inferred from multiple sequence alignments. Covers the full spectrum of the field, from alignment algorithms to scoring methods, practical techniques, and alignment tools and their evaluations Describes theories and developments of scoring functions and scoring matrices Examines phylogeny estimation and large-scale homology search Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications is a reference for researchers, engineers, graduate and post-graduate students in bioinformatics, and system biology and molecular biologists. Ken Nguyen, PhD, is an associate professor at Clayton State University, GA, USA. He received his PhD, MSc and BSc degrees in computer science all from Georgia State University. His research interests are in databases, parallel and distribute computing and bioinformatics. He was a Molecular Basis of Disease fellow at Georgia State and is the recipient of the highest graduate honor at Georgia State, the William M. Suttles Graduate Fellowship. Xuan Guo, PhD, is a postdoctoral associate at Oak Ridge National Lab, USA. He received his PhD degree in computer science from Georgia State University in 2015. His research interests are in bioinformatics, machine leaning, and cloud computing. He is an editorial assistant of International Journal of Bioinformatics Research and Applications. Yi Pan, PhD, is a Regents' Professor of Computer Science and an Interim Associate Dean and Chair of Biology at Georgia State University. He received his BE and ME in computer engineering from Tsinghua University in China and his PhD in computer science from the University of Pittsburgh. Dr. Pan's research interests include parallel and distributed computing, optical networks, wireless networks and bioinformatics. He has published more than 180 journal papers with about 60 papers published in various IEEE/ACM journals. He is co-editor along with Albert Y. Zomaya of the Wiley Series in Bioinformatics.
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Computational Methods For Next Generation Sequencing Data Analysis

136,41€ + 10% CARD

 Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
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Computational Methods For Next Generation Sequencing Data Analysis

140,53€ + 10% CARD

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 Aiming to foster future collaborations between researchers in algorithms, bioinformatics, and molecular biology, this book serves as an up-to-date survey of the most important recent developments and computational challenges in various application areas of next-generation sequencing technologies.
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Evolutionary Computation In Gene Regulatory Network Research

161,58€ + 10% CARD

 Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
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Pattern Recognition In Computational Molecular Biology - Techniques And Approaches

by Albert Y. Zomaya, Costas Iliopoulos, Jason T. L. Wang e Mourad Elloumi

161,58€ + 10% CARD

 A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.
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Evolutionary Computation In Gene Regulatory Network Research

164,86€ + 10% CARD

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 Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC).
ENGLISH

Pattern Recognition In Computational Molecular Biology - Techniques And Approaches

by Albert Y. (University Of Western Australia) Zomaya, Costas (King'S College London, Uk) Iliopoulos, Jason T. L. (New Jersey Institute Of Technology, Usa) Wang e Mourad (University Of Tunis-El Manar, Tunisia) Elloumi

164,86€ + 10% CARD

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 A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology.
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Biological Knowledge Discovery Handbook - Preprocessing, Mining And Postprocessing Of Biological Data

211,93€ + 10% CARD

 Molecular biology is undergoing exponential growth in both the volume and complexity of biological data. This book offers the first comprehensive overview of data mining, preprocessing, postprocessing, and storage for biological data.
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Algorithmic And Artificial Intelligence Methods For Protein Bioinformatics

143,03€ + 10% CARD

 Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.
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Biological Knowledge Discovery Handbook - Preprocessing, Mining And Postprocessing Of Biological Data

220,29€ + 10% CARD

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 Molecular biology is undergoing exponential growth in both the volume and complexity of biological data. This book offers the first comprehensive overview of data mining, preprocessing, postprocessing, and storage for biological data.
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Elements Of Computational Systems Biology

175,68€ + 10% CARD

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 This comprehensive reference presents cutting-edge and long-ranging research in computational systems biology. It is written by leading experts and covers a range of topics from modeling and learning biological systems to the impact of computational systems biology on drug design and medicine.
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Rough-Fuzzy Pattern Recognition - Applications In Bioinformatics And Medical Imaging

by Pradipta Maji e Sankar K. Pal

131,11€ + 10% CARD

 This book provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models. Based on the existing as well as new results, the book is structured according to the major phases of a pattern recognition system (e.g.
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Classification Analysis Of Dna Microarray Data

by Leif E. Peterson

125,66€ + 10% CARD

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 The rapid uncontrolled growth of classification methods in DNA microarray studies has resulted in a body of information scattered throughout literature, numerous conference proceedings, and others. This book brings together many of the unsupervised and supervised classification methods now dispersed in the literature.
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Mathematical And Computational Methods In Biomechanics Of Human Skeletal Systems - An Introduction

by Ivan Hlavacek, Jiri Nedoma, Jiri Stehlik, Josef Danek, Petra Preckova e Tatjana Dostalova

149,66€ + 10% CARD

 * Provides the orthopedic, biomechanical and mathematical basis for the mathematical simulation of surgical techniques * Assists designers, researchers and manufacturers of joint implants who require the results of suggested experiments to improve existing shapes or to design the new shapes.
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Mathematics Of Bioinformatics - Theory, Methods And Applications

by Matthew He e Sergey Petoukhov

139,06€ + 10% CARD

 The mathematical analysis of the structures of molecular systems has essential meaning for bioinformatics, biomathematics, and biotechnology. This book covers the connection and integration between fundamental mathematical methods and biological sequences, structures, and networks.
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Introduction To Protein Structure Prediction - Methods And Algorithms

176,16€ + 10% CARD

 A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
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Algorithms In Computational Molecular Biology - Techniques, Approaches And Applications

206,63€ + 10% CARD

 * Cutting edge source for algorithms dealing with most studied, most important, and newest topics in Computational Molecular Biology. * Offers both the fundamental introductory information and advanced technical data, with related problems.
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Computational Intelligence And Pattern Analysis In Biology Informatics

by Jason T. Wang, Sanghamitra Bandyopadhyay e Ujjwal Maulik

149,66€ + 10% CARD

 An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.
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Elements Of Computational Systems Biology

169,53€ + 10% CARD

 This comprehensive reference presents cutting-edge and long-ranging research in computational systems biology. It is written by leading experts and covers a range of topics from modeling and learning biological systems to the impact of computational systems biology on drug design and medicine.
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Analysis Of Biological Networks

145,68€ + 10% CARD

 An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science.
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Bioinformatics Algorithms - Techniques And Applications

200,01€ + 10% CARD

 Bioinformatics Algorithms: Techniques and Applications targets the future collaboration of researchers in algorithms, bioinformatics, and molecular biology. It addresses critical bioinformatics research areas of protein-protein interaction, molecular modeling in drug design, and structural biology.
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Biomolecular Networks - Methods And Applications In Systems Biology

150,98€ + 10% CARD

 Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms.
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Machine Learning In Bioinformatics

162,91€ + 10% CARD

 An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
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Grid Computing For Bioinformatics And Computational Biology

202,66€ + 10% CARD

 The only single, up-to-date source for Grid issues in bioinformatics and biology Bioinformatics is fast emerging as an important discipline for academic research and industrial applications, creating a need for the use of Grid computing techniques for large-scale distributed applications.
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