Books in the Chapman & Hall/CRC Computational Biology Series series

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.

This book covers Metabolomics, the scientific study of the chemical processes of the metabolic system. It is based on a course taught to masters and postgraduate students of bioinformatics and statistics at the European Bioinformatics Institute at Cambridge.

An invaluable resource for computational biologists and researchers from other fields seeking an introduction to the topic, Chromatin: Structure, Dynamics, Regulation offers comprehensive coverage of this dynamic interdisciplinary field, from the basics to the latest research. Computational methods from statistical physics and bioinformat

The text provides unified frameworks, basic knowledge and efficient computational tools for analyzing growing large, complex and diverse genomic, epigenomic, physiological and image data. It introduces currently developed statistical methods and software for big genomic and epigenomic data analysis with real-world examples and case studies.

Emerging genomic, epigenomic, sensing and image technologies will produce massive, dimensional genomic, epigenomic, physiological, image and clinical data. The book is designed to introduce the currently developed statistical methods and software for big genomic and epigenomic data analysis.

Virus Bioinformatics reviews state-of-the-art bioinformatics algorithms and recent advances in data analysis in virology.

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.

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.

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.

Focuses on the interface between statistical physics and bioinformatics. Introducing both equilibrium and nonequilibrium statistical mechanics, this work applies these methods to understand and model the properties of various biomolecules and biological networks at the systems level.

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.

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.

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.

With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states.

This book covers Metabolomics, the scientific study of the chemical processes of the metabolic system. It is based on a course taught to masters and postgraduate students of bioinformatics and statistics at the European Bioinformatics Institute at Cambridge.

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.

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.

Emerging genomic, epigenomic, sensing and image technologies will produce massive, dimensional genomic, epigenomic, physiological, image and clinical data. The book is designed to introduce the currently developed statistical methods and software for big genomic and epigenomic data analysis.

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.