Böcker i Chapman & Hall/CRC Computational Biology Series-serien

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.

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.

This book is a practical guide to the nuts and bolts of Big Data, enabling readers to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances readers¿ computational and quantitative skills so that they can exploit the Big Data being generated in the current omics era.

Suitable for graduate-level researchers in statistics and biology, this book presents a snapshot of current trends in Bayesian phylogenetic research. It emphasizes model selection, reflecting recent interest in accurately estimating marginal likelihoods. The book discusses new approaches to improve mixing in Bayesian phylogenetic analyses in which the tree topology varies. It also covers divergence time estimation, biologically realistic models, and the burgeoning interface between phylogenetics and population genetics.

This book shows how to perform computations with Python scripts in the Chimera environment. It covers topics that deal primarily with protein structure and encourages mathematical analysis by providing a firm foundation for computations. Through more than 60 exercises that involve the development of Python scripts, the book gives readers concrete guidance on using the scripting capabilities of Chimera. Python scripts and other material are available on a supplementary website.

Drawn from the authors¿ decade-long work in the cancer computational systems biology laboratory at Institut Curie, this self-contained guide explains how to apply computational systems biology approaches to cancer research. Suitable for readers in both computational and life sciences, the book provides proven techniques and tools for cancer bioinformatics and systems biology research. It explores how computational systems biology can help fight cancer in three essential aspects: categorizing tumors, finding new targets, and designing improved and tailored therapeutic strategies.

Presents an introduction to cluster analysis and algorithms in the context of drug discovery clustering applications. This book provides an understanding of the applications in clustering large combinatorial libraries for compound acquisition, HTS results, 3D lead hopping, gene expression for toxicity studies, and protein reaction data.

A guide for applying basic informatics algorithms to medical data sets. It includes examples using three most common programming languages (Perl, Python, and Ruby). It provides basic methods for retrieving, organizing, merging, and analyzing their data sources. It covers building blocks, primary tasks of medical informatics, and medical discovery.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This book is intended to be an introductory bioinformatics textbook for mathematicians and computer scientists. It focuses on using algorithms and discrete mathematics to solve biological problems. The book systematically describes biological applications, the corresponding mathematical/computational problems, and various algorithmic solutions. It also discusses the practical use of various algorithmic methods and describes what algorithms should be used in different situations.

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.

Replaces the commercial software with the open source R computing environment. This title contains chapters on cutting-edge microarray topics and provides the R code on an accompanying CD-ROM. It bridges the gap between an introduction to data analysis and advanced material for performing data analysis.

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.

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.

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.

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 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.