Probabilistic Methods For Bioinformatics
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Probabilistic Methods For Bioinformatics
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Author : Richard E. Neapolitan
language : en
Publisher: Morgan Kaufmann
Release Date : 2009-06-12
Probabilistic Methods For Bioinformatics written by Richard E. Neapolitan and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-12 with Computers categories.
The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. Shares insights about when and why probabilistic methods can and cannot be used effectively; Complete review of Bayesian networks and probabilistic methods with a practical approach.
Probabilistic Modeling In Bioinformatics And Medical Informatics
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Author : Dirk Husmeier
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-06
Probabilistic Modeling In Bioinformatics And Medical Informatics written by Dirk Husmeier and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-06 with Computers categories.
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Statistical Methods In Bioinformatics
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Author : Warren J. Ewens
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Statistical Methods In Bioinformatics written by Warren J. Ewens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Medical categories.
There was a real need for a book that introduces statistics and probability as they apply to bioinformatics. This book presents an accessible introduction to elementary probability and statistics and describes the main statistical applications in the field.
Bayesian Methods In Structural Bioinformatics
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Author : Thomas Hamelryck
language : en
Publisher: Springer
Release Date : 2012-03-23
Bayesian Methods In Structural Bioinformatics written by Thomas Hamelryck and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-23 with Medical categories.
This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
Handbook Of Statistical Bioinformatics
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Author : Henry Horng-Shing Lu
language : en
Publisher: Springer Nature
Release Date : 2022-12-08
Handbook Of Statistical Bioinformatics written by Henry Horng-Shing Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Science categories.
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.
Statistical Methods In Bioinformatics
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Author : Warren J. Ewens
language : en
Publisher: Springer
Release Date : 2008-11-01
Statistical Methods In Bioinformatics written by Warren J. Ewens and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-01 with Science categories.
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)
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Raphaël Mourad
language : en
Publisher: OUP Oxford
Release Date : 2014-09-18
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Raphaël Mourad and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-18 with Science categories.
Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Pattern Discovery In Bioinformatics
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Author : Laxmi Parida
language : en
Publisher: CRC Press
Release Date : 2007-07-04
Pattern Discovery In Bioinformatics written by Laxmi Parida and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-04 with Computers categories.
The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema
Statistical Bioinformatics
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Author : Jae K. Lee
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Statistical Bioinformatics written by Jae K. Lee and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Medical categories.
This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.
Probabilistic Boolean Networks
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Author : Ilya Shmulevich
language : en
Publisher: SIAM
Release Date : 2010-01-21
Probabilistic Boolean Networks written by Ilya Shmulevich and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-21 with Mathematics categories.
The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.