Wiley in Bioinformatics

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. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing. Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. “Rough-Fuzzy Pattern Recognition” examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: • Soft computing in pattern recognition and data mining • A mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set • Selection of non-redundant and relevant features of real-valued data sets • Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis • Segmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text-covering the latest findings as well as directions for future research-is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

Cutting-edge solutions to current problems in orthopedics, supported by modeling and numerical analysis Despite the current successful methods and achievements of good joint implantations, it is essential to further optimize the shape of implants so they may better resist extreme long-term mechanical demands. This book provides the orthopedic, biomechanical, and mathematical basis for the simulation of surgical techniques in orthopedics. It focuses on the numerical modeling of total human joint replacements and simulation of their functions, along with the rigorous biomechanics of human joints and other skeletal parts. The book includes: • An introduction to the anatomy and biomechanics of the human skeleton, biomaterials, and problems of alloarthroplasty • The definition of selected simulated orthopedic problems • Constructions of mathematical model problems of the biomechanics of the human skeleton and its parts • Replacement parts of the human skeleton and corresponding mathematical model problems • Detailed mathematical analyses of mathematical models based on functional analysis and finite element methods • Biomechanical analyses of particular parts of the human skeleton, joints, and corresponding replacements • A discussion of the problems of data processing from nuclear magnetic resonance imaging and computer tomography This timely book offers a wealth of information on the current research in this field. The theories presented are applied to specific problems of orthopedics. Numerical results are presented and discussed from both biomechanical and orthopedic points of view and treatment methods are also briefly addressed. Emphasis is placed on the variational approach to the investigated model problems while preserving the orthopedic nature of the investigated problems. The book also presents a study of algorithmic procedures based on these simulation models. This is a highly useful tool for designers, researchers, and manufacturers of joint implants who require the results of suggested experiments to improve existing shapes or to design new shapes. It also benefits graduate students in orthopedics, biomechanics, and applied mathematics.

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.

“Mathematics of Bioinformatics: Theory, Methods, and Applications” provides a comprehensive format for connecting and integrating information derived from mathematical methods and applying it to the understanding of biological sequences, structures, and networks. Each chapter is divided into a number of sections based on the bioinformatics topics and related mathematical theory and methods. Each topic of the section is comprised of the following three parts: an introduction to the biological problems in bioinformatics; a presentation of relevant topics of mathematical theory and methods to the bioinformatics problems introduced in the first part; an integrative overview that draws the connections and interfaces between bioinformatics problems/issues and mathematical theory/methods/applications.

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. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers. • Chapters authored by leading researchers in CI in biology informatics. • Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. • Supplementary material included: program code and relevant data sets correspond to chapters.

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

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.

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 researchers interested in Computational Molecular Biology.

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.

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.

The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data-and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)-providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing-also known as data mining-and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING • Part A: Biological Data Management • Part B: Biological Data Modeling • Part C: Biological Feature Extraction • Part D Biological Feature Selection BIOLOGICAL DATA MINING • Part E: Regression Analysis of Biological Data • Part F Biological Data Clustering • Part G: Biological Data Classification • Part H: Association Rules Learning from Biological Data • Part I: Text Mining and Application to Biological Data • Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, “Biological Knowledge Discovery Handbook” is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

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.