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This will be the first book to systematically describe the process and provide corresponding methods for analysing data generated from genetic- and epigenetic-studies. Specifically, the book aims to provide a "pipe line" for genetic and epigenetic data analysis starting from raw genome- and epigenome-scale data.

Applying complex systems science to biology, this book develops mathematical models for understanding biological systems. Based on his one-semester course, the author suggests appropriate control strategies to mediate the effects of past and future pandemics, assuming no prior knowledge of mathematics. Each chapter presents exercises with worked solutions as well as computational and research projects. Topics covered include pattern formation and flocking behavior, the interaction of autonomous agents, hierarchical and structured network topologies, epidemiology, biomedical signal processing, computational neurophysiology, and population dynamics. A solutions manual is available for qualifying instructors.

Written for students and researchers in systems biology, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological networks, highlighting simple, recurring circuit elements that make up the regulation of cells and tissues.

This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes. It takes readers through the whole data analysis workflow. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools.

This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Since R has many packages, even experienced researchers look for how particular functions are used in an analysis workflow.

Emphasizing the search for patterns within and between biological sequences, trees, and graphs, this book shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data.

The SeqAn project was initiated to offer access to the algorithms needed by researchers in computational biology and bioinformatics. This book helps you in rapid prototyping of algorithms in the field. It is suitable for bioinformaticians.

Introduction to Proteins shows how proteins can be analyzed in multiple ways. It refers to the roles of proteins and enzymes in diverse contexts and everyday applications, including medical disorders, drugs, toxins, chemical warfare, and animal behavior.

Presents the modeling, analysis and design methods for systems biology. This work discusses how to examine experimental data to learn about mathematical models, develop efficient abstraction and simulation methods to analyze these models, and use analytical methods to design new circuits.

Microarrays allow researchers to simultaneously monitor the expression of thousands of genes. Independent of the platform and analysis methods used, the result of a microarray experiment is a list of differentially expressed genes. Presenting a unified analysis of the field, this book explores the tools available to better understand the underlying biological phenomena of differentially expressed genes. It focuses on two major analytic approaches: 1) ontological profiling and 2) gene interaction networks and known pathways. The author presents the fundamentals and tools for each approach.

This second edition incorporates important advancements made in bioinformatics over the last decade. Focusing on practical problem solving, it works through sequence analysis from simple gene family analysis to expression analysis with microarrays and next-gen sequencing.

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics' data. This book describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research.

Exploring high speed computational methods to extrapolate to the rest of the protein universe, this book considers the most significant problems occupying those looking to identify the biological properties and functional roles of proteins.

Covers the various informatics methods pertaining to the study of glycans, which provide crucial functional roles in many biological processes. This book supplies the necessary background information, including glycan classes, motifs, and nomenclature. It offers a list of relevant databases and resources on glycobiology.

Presents techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. This book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments.

From the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, this book describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. It addresses the ways to store and retrieve biological data.

Gene expression studies merge three disciplines with different historical backgrounds: molecular biology, bioinformatics, and biostatistics. This book explains the entire process of a gene expression study from conception to interpretation. It describes technical and statistical methods conceptually with illustrative examples.

Suitable for advanced undergraduates in computer science programs, this title covers major themes of bio-inspired computing, including cellular automata, molecular computation, genetic algorithms, and neural networks. It provides theoretical and coding exercises.

The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation to glean meaningful information in systems-level biology. This book introduces the concepts and theories of systems biology and the applications of systems biology in cancer research.

Describing the characteristics and limitations of key algorithms, this book covers various aspects of chemoinformatics, including structure representation, molecular descriptors, similarity search, virtual screening, and structure-property model generation and validation.