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Chapman & Hall/CRC Computational Biology Series


About the Series

This series aims to capture new developments in computational biology, as well as high-quality work summarizing or contributing to more established topics. Publishing a broad range of reference works, textbooks, and handbooks, the series is designed to appeal to students, researchers, and professionals in all areas of computational biology, including genomics, proteomics, and cancer computational biology, as well as interdisciplinary researchers involved in associated fields, such as bioinformatics and systems biology.

For more information or to submit a book proposal, please contact <a href="[email protected]">Elliott Morsia</a> ([email protected]).

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Python for Bioinformatics

Python for Bioinformatics

2nd Edition

By Sebastian Bassi
July 10, 2017

In today's data driven biology, programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience, Python for Bioinformatics, Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of ...

Chromatin Structure, Dynamics, Regulation

Chromatin: Structure, Dynamics, Regulation

1st Edition

By Ralf Blossey
July 21, 2017

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...

Biological Sequence Analysis Using the SeqAn C++ Library

Biological Sequence Analysis Using the SeqAn C++ Library

1st Edition

By Andreas Gogol-Döring, Knut Reinert
June 14, 2017

An Easy-to-Use Research Tool for Algorithm Testing and Development Before the SeqAn project, there was clearly a lack of available implementations in sequence analysis, even for standard tasks. Implementations of needed algorithmic components were either unavailable or hard to access in third-party...

Cancer Systems Biology

Cancer Systems Biology

1st Edition

Edited By Edwin Wang
June 14, 2017

The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation methods to glean meaningful information in systems-level biology. Applying this approach to the underlying molecular mechanisms of tumorigenesis, ...

Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R

Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R

1st Edition

By Gabriel Valiente
June 14, 2017

Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis...

Meta-analysis and Combining Information in Genetics and Genomics

Meta-analysis and Combining Information in Genetics and Genomics

1st Edition

By Rudy Guerra, Darlene R. Goldstein
June 14, 2017

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal ...

Algorithms for Next-Generation Sequencing

Algorithms for Next-Generation Sequencing

1st Edition

By Wing-Kin Sung
May 24, 2017

Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for ...

Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

1st Edition

By Alan Moses
December 15, 2016

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 ...

Big Data Analysis for Bioinformatics and Biomedical Discoveries

Big Data Analysis for Bioinformatics and Biomedical Discoveries

1st Edition

Edited By Shui Qing Ye
December 22, 2015

Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, ...

RNA-seq Data Analysis A Practical Approach

RNA-seq Data Analysis: A Practical Approach

1st Edition

By Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong
September 19, 2014

The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine ...

Computational and Visualization Techniques for Structural Bioinformatics Using Chimera

Computational and Visualization Techniques for Structural Bioinformatics Using Chimera

1st Edition

By Forbes J. Burkowski
July 29, 2014

A Step-by-Step Guide to Describing Biomolecular Structure Computational and Visualization Techniques for Structural Bioinformatics Using Chimera shows how to perform computations with Python scripts in the Chimera environment. It focuses on the three core areas needed to study structural ...

Bayesian Phylogenetics Methods, Algorithms, and Applications

Bayesian Phylogenetics: Methods, Algorithms, and Applications

1st Edition

Edited By Ming-Hui Chen, Lynn Kuo, Paul O. Lewis
May 27, 2014

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, ...

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