Plant Systems Biology

Plant Systems Biology

Author: Sacha Baginsky

Publisher: Springer Science & Business Media

Published: 2007-06-25

Total Pages: 362

ISBN-13: 376437439X

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This volume aims to provide a timely view of the state-of-the-art in systems biology. The editors take the opportunity to define systems biology as they and the contributing authors see it, and this will lay the groundwork for future studies. The volume is well-suited to both students and researchers interested in the methods of systems biology. Although the focus is on plant systems biology, the proposed material could be suitably applied to any organism.


Genomics Data Analysis

Genomics Data Analysis

Author: David R. Bickel

Publisher: CRC Press

Published: 2019-09-24

Total Pages: 141

ISBN-13: 1000706915

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Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research. Key Features: * dice games and exercises, including one using interactive software, for teaching the concepts in the classroom * examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data * gradual introduction to the mathematical equations needed * how to choose between different methods of multiple hypothesis testing * how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates * guidance through the minefield of current criticisms of p values * material on non-Bayesian prior p values and posterior p values not previously published


Data Mining for Genomics and Proteomics

Data Mining for Genomics and Proteomics

Author: Darius M. Dziuda

Publisher: John Wiley & Sons

Published: 2010-07-16

Total Pages: 348

ISBN-13: 0470593407

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Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.


Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Author: Robert Gentleman

Publisher: Springer Science & Business Media

Published: 2005-12-29

Total Pages: 478

ISBN-13: 0387293620

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Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.


Bioinformatics Research and Development

Bioinformatics Research and Development

Author: Sepp Hochreiter

Publisher: Springer

Published: 2007-05-21

Total Pages: 497

ISBN-13: 354071233X

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This book constitutes the refereed proceedings of the First International Bioinformatics Research and Development Conference, BIRD 2007, held in Berlin, Germany in March 2007. The 36 revised full papers are organized in topical sections on microarray and systems biology and networks, medical, SNPs, genomics, systems biology, sequence analysis and coding, proteomics and structure, databases, Web and text analysis.


Bayesian Analysis of Gene Expression Data

Bayesian Analysis of Gene Expression Data

Author: Bani K. Mallick

Publisher: John Wiley & Sons

Published: 2009-07-20

Total Pages: 252

ISBN-13: 9780470742815

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The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.


Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Author: Susmita Datta

Publisher: Springer

Published: 2016-12-15

Total Pages: 294

ISBN-13: 3319458094

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This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.


Bioinformatics for Systems Biology

Bioinformatics for Systems Biology

Author: Stephen Krawetz

Publisher: Springer Science & Business Media

Published: 2008-12-11

Total Pages: 623

ISBN-13: 1597454400

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Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward.