Handbook Of Statistical Systems Biology

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Handbook of Statistical Systems Biology

Michael Stumpf,David J. Balding,Mark Girolami

Author : Michael Stumpf,David J. Balding,Mark Girolami
Publisher : John Wiley & Sons
Page : 624 pages
File Size : 53,9 Mb
Release : 2011-09-09
Category : Science
ISBN : 9781119952046

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Handbook of Statistical Systems Biology by Michael Stumpf,David J. Balding,Mark Girolami Pdf

Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Handbook of Statistical Bioinformatics

Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao

Author : Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao
Publisher : Springer Nature
Page : 406 pages
File Size : 50,9 Mb
Release : 2022-12-08
Category : Science
ISBN : 9783662659021

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Handbook of Statistical Bioinformatics by Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao Pdf

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Applied Statistics for Network Biology

Matthias Dehmer,Frank Emmert-Streib,Armin Graber,Armindo Salvador

Author : Matthias Dehmer,Frank Emmert-Streib,Armin Graber,Armindo Salvador
Publisher : John Wiley & Sons
Page : 441 pages
File Size : 47,5 Mb
Release : 2011-04-08
Category : Medical
ISBN : 9783527638086

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Applied Statistics for Network Biology by Matthias Dehmer,Frank Emmert-Streib,Armin Graber,Armindo Salvador Pdf

The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

Handbook of Research on Systems Biology Applications in Medicine

Daskalaki, Andriani

Author : Daskalaki, Andriani
Publisher : IGI Global
Page : 982 pages
File Size : 53,9 Mb
Release : 2008-11-30
Category : Technology & Engineering
ISBN : 9781605660776

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Handbook of Research on Systems Biology Applications in Medicine by Daskalaki, Andriani Pdf

"This book highlights the use of systems approaches including genomic, cellular, proteomic, metabolomic, bioinformatics, molecular, and biochemical, to address fundamental questions in complex diseases like cancer diabetes but also in ageing"--Provided by publisher.

Statistical Modeling and Machine Learning for Molecular Biology

Alan Moses

Author : Alan Moses
Publisher : CRC Press
Page : 255 pages
File Size : 52,7 Mb
Release : 2017-01-06
Category : Mathematics
ISBN : 9781482258622

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Statistical Modeling and Machine Learning for Molecular Biology by Alan Moses Pdf

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.

Algebraic Statistics for Computational Biology

L. Pachter,B. Sturmfels

Author : L. Pachter,B. Sturmfels
Publisher : Cambridge University Press
Page : 440 pages
File Size : 53,6 Mb
Release : 2005-08-22
Category : Mathematics
ISBN : 0521857007

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Algebraic Statistics for Computational Biology by L. Pachter,B. Sturmfels Pdf

This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.

Computational Biology

Ralf Blossey

Author : Ralf Blossey
Publisher : CRC Press
Page : 285 pages
File Size : 50,5 Mb
Release : 2019-06-11
Category : Computers
ISBN : 9780429994616

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Computational Biology by Ralf Blossey Pdf

Computational biology has developed rapidly during the last two decades following the genomic revolution which culminated in the sequencing of the human genome. More than ever it has developed into a field which embraces computational methods from different branches of the exact sciences: pure and applied mathematics, computer science, theoretical physics. This Second Edition provides a solid introduction to the techniques of statistical mechanics for graduate students and researchers in computational biology and biophysics. Material has been reorganized to clarify equilbrium and nonequilibrium aspects of biomolecular systems Content has been expanded, in particular in the treatment of the electrostatic interactions of biomolecules and the application of non-equilibrium statistical mechanics to biomolecules New network-based approaches for the study of proteins are presented. All treated topics are put firmly in the context of the current research literature, allowing the reader to easily follow an individual path into a specific research field. Exercises and Tasks accompany the presentations of the topics with the intention of enabling the readers to test their comprehension of the developed basic concepts.

Statistical Methods in Bioinformatics

Warren J. Ewens,Gregory R. Grant

Author : Warren J. Ewens,Gregory R. Grant
Publisher : Springer Science & Business Media
Page : 598 pages
File Size : 53,7 Mb
Release : 2006-03-30
Category : Science
ISBN : 9780387266480

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Statistical Methods in Bioinformatics by Warren J. Ewens,Gregory R. Grant Pdf

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods. The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized. The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text. Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science. Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999. Comments on the first edition: "This book would be an ideal text for a postgraduate course...[and] is equally well suited to individual study.... I would recommend the book highly." (Biometrics) "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces." (Naturwissenschaften) "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details." (Journal American Statistical Association) "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book." (Metrika)

Stochastic Modelling for Systems Biology, Second Edition

Darren J. Wilkinson

Author : Darren J. Wilkinson
Publisher : CRC Press
Page : 365 pages
File Size : 48,8 Mb
Release : 2011-11-09
Category : Mathematics
ISBN : 9781439837726

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Stochastic Modelling for Systems Biology, Second Edition by Darren J. Wilkinson Pdf

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. New in the Second Edition All examples have been updated to Systems Biology Markup Language Level 3 All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation Simple modelling of "extrinsic" and "intrinsic" noise An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Stochastic Modelling for Systems Biology, Third Edition

Darren J. Wilkinson

Author : Darren J. Wilkinson
Publisher : CRC Press
Page : 292 pages
File Size : 49,5 Mb
Release : 2018-12-07
Category : Mathematics
ISBN : 9781351000895

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Stochastic Modelling for Systems Biology, Third Edition by Darren J. Wilkinson Pdf

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Simulation Algorithms for Computational Systems Biology

Luca Marchetti,Corrado Priami,Vo Hong Thanh

Author : Luca Marchetti,Corrado Priami,Vo Hong Thanh
Publisher : Springer
Page : 238 pages
File Size : 41,5 Mb
Release : 2017-09-27
Category : Computers
ISBN : 9783319631134

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Simulation Algorithms for Computational Systems Biology by Luca Marchetti,Corrado Priami,Vo Hong Thanh Pdf

This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.

Statistical Modeling for Biological Systems

Anthony Almudevar,David Oakes,Jack Hall

Author : Anthony Almudevar,David Oakes,Jack Hall
Publisher : Springer Nature
Page : 361 pages
File Size : 47,8 Mb
Release : 2020-03-11
Category : Medical
ISBN : 9783030346751

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Statistical Modeling for Biological Systems by Anthony Almudevar,David Oakes,Jack Hall Pdf

This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.

Computational Genomics with R

Altuna Akalin

Author : Altuna Akalin
Publisher : CRC Press
Page : 462 pages
File Size : 54,9 Mb
Release : 2020-12-16
Category : Mathematics
ISBN : 9781498781862

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Computational Genomics with R by Altuna Akalin Pdf

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Handbook of Statistical Genetics

David J. Balding,Martin Bishop,Chris Cannings

Author : David J. Balding,Martin Bishop,Chris Cannings
Publisher : John Wiley & Sons
Page : 1616 pages
File Size : 48,7 Mb
Release : 2008-06-10
Category : Science
ISBN : 0470997621

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Handbook of Statistical Genetics by David J. Balding,Martin Bishop,Chris Cannings Pdf

The Handbook for Statistical Genetics is widely regarded as the reference work in the field. However, the field has developed considerably over the past three years. In particular the modeling of genetic networks has advanced considerably via the evolution of microarray analysis. As a consequence the 3rd edition of the handbook contains a much expanded section on Network Modeling, including 5 new chapters covering metabolic networks, graphical modeling and inference and simulation of pedigrees and genealogies. Other chapters new to the 3rd edition include Human Population Genetics, Genome-wide Association Studies, Family-based Association Studies, Pharmacogenetics, Epigenetics, Ethic and Insurance. As with the second Edition, the Handbook includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between the chapters, tying the different areas together. With heavy use of up-to-date examples, real-life case studies and references to web-based resources, this continues to be must-have reference in a vital area of research. Edited by the leading international authorities in the field. David Balding - Department of Epidemiology & Public Health, Imperial College An advisor for our Probability & Statistics series, Professor Balding is also a previous Wiley author, having written Weight-of-Evidence for Forensic DNA Profiles, as well as having edited the two previous editions of HSG. With over 20 years teaching experience, he’s also had dozens of articles published in numerous international journals. Martin Bishop – Head of the Bioinformatics Division at the HGMP Resource Centre As well as the first two editions of HSG, Dr Bishop has edited a number of introductory books on the application of informatics to molecular biology and genetics. He is the Associate Editor of the journal Bioinformatics and Managing Editor of Briefings in Bioinformatics. Chris Cannings – Division of Genomic Medicine, University of Sheffield With over 40 years teaching in the area, Professor Cannings has published over 100 papers and is on the editorial board of many related journals. Co-editor of the two previous editions of HSG, he also authored a book on this topic.

Statistics and Data Analysis for Microarrays Using R and Bioconductor

Sorin Draghici

Author : Sorin Draghici
Publisher : CRC Press
Page : 1036 pages
File Size : 44,9 Mb
Release : 2016-04-19
Category : Computers
ISBN : 9781439809761

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Statistics and Data Analysis for Microarrays Using R and Bioconductor by Sorin Draghici Pdf

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on,

Handbook Of Statistical Systems Biology

Handbook of Statistical Systems Biology by Michael Stumpf,David J. Balding,Mark Girolami Pdf

Handbook of Statistical Bioinformatics by Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao Pdf

Applied Statistics for Network Biology by Matthias Dehmer,Frank Emmert-Streib,Armin Graber,Armindo Salvador Pdf

Handbook of Research on Systems Biology Applications in Medicine by Daskalaki, Andriani Pdf

Statistical Modeling and Machine Learning for Molecular Biology by Alan Moses Pdf

Algebraic Statistics for Computational Biology by L. Pachter,B. Sturmfels Pdf

Computational Biology by Ralf Blossey Pdf

Statistical Methods in Bioinformatics by Warren J. Ewens,Gregory R. Grant Pdf

Stochastic Modelling for Systems Biology, Second Edition by Darren J. Wilkinson Pdf

Stochastic Modelling for Systems Biology, Third Edition by Darren J. Wilkinson Pdf

Simulation Algorithms for Computational Systems Biology by Luca Marchetti,Corrado Priami,Vo Hong Thanh Pdf

Statistical Modeling for Biological Systems by Anthony Almudevar,David Oakes,Jack Hall Pdf

Computational Genomics with R by Altuna Akalin Pdf

Handbook of Statistical Genetics by David J. Balding,Martin Bishop,Chris Cannings Pdf

Statistics and Data Analysis for Microarrays Using R and Bioconductor by Sorin Draghici Pdf

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