Probabilistic Methods for Bioinformatics Book
Score: 1
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Probabilistic Methods for Bioinformatics


  • Author : Richard E. Neapolitan
  • Publisher : Morgan Kaufmann
  • Release Date : 2009-06-12
  • Genre: Computers
  • Pages : 424
  • ISBN 10 : 0080919367
  • Total Read : 96
  • File Size : 14,9 Mb

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Probabilistic Methods for Bioinformatics Summary:

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 Methods for Bionformatics Book

Probabilistic Methods for Bionformatics


  • Author : Richard E. Neapolitan
  • Publisher : Morgan Kaufmann
  • Release Date : 2009
  • Genre: Computers
  • Pages : 406
  • ISBN 10 : 0123704766
  • Total Read : 72
  • File Size : 16,9 Mb

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Probabilistic Methods for Bionformatics Summary:

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. This text explains the application of probability and statistics, in particular Bayesian networks, to genetics.

Probabilistic Modeling in Bioinformatics and Medical Informatics Book

Probabilistic Modeling in Bioinformatics and Medical Informatics


  • Author : Dirk Husmeier
  • Publisher : Springer Science & Business Media
  • Release Date : 2006-03-30
  • Genre: Computers
  • Pages : 508
  • ISBN 10 : 9781846281198
  • Total Read : 98
  • File Size : 12,7 Mb

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Probabilistic Modeling in Bioinformatics and Medical Informatics Summary:

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.

Biological Sequence Analysis Book
Score: 4
From 8 Ratings

Biological Sequence Analysis


  • Author : Richard Durbin
  • Publisher : Cambridge University Press
  • Release Date : 1998-04-23
  • Genre: Science
  • Pages : 372
  • ISBN 10 : 0521629713
  • Total Read : 59
  • File Size : 12,9 Mb

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Biological Sequence Analysis Summary:

Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

Bayesian Methods in Structural Bioinformatics Book

Bayesian Methods in Structural Bioinformatics


  • Author : Thomas Hamelryck
  • Publisher : Springer
  • Release Date : 2012-03-23
  • Genre: Medical
  • Pages : 399
  • ISBN 10 : 9783642272257
  • Total Read : 69
  • File Size : 9,5 Mb

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Bayesian Methods in Structural Bioinformatics Summary:

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.

Statistical Methods in Bioinformatics Book

Statistical Methods in Bioinformatics


  • Author : Warren J. Ewens
  • Publisher : Springer Science & Business Media
  • Release Date : 2006-03-30
  • Genre: Science
  • Pages : 598
  • ISBN 10 : 9780387266480
  • Total Read : 71
  • File Size : 16,5 Mb

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Statistical Methods in Bioinformatics Summary:

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 aspe

Bioinformatics Book

Bioinformatics


  • Author : Ron D. Appel
  • Publisher : World Scientific
  • Release Date : 2009
  • Genre: Science
  • Pages : 464
  • ISBN 10 : 9789812838780
  • Total Read : 82
  • File Size : 13,8 Mb

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Bioinformatics Summary:

Biological research and recent technological advances have resulted in an enormous increase in research data that require large storage capacities, powerful computing resources, and accurate data analysis algorithms. Bioinformatics is the field that provides these resources to life science researchers. The Swiss Institute of Bioinformatics (SIB), which has celebrated its 10th anniversary in 2008, is an institution of national importance, recognized worldwide for its state-of-the-art work. Organized as a federation of bioinformatics research groups from Swiss universities and research institutes, the SIB provides services to the life science community that are highly appreciated worldwide, and coordinates research and education in bioinformatics nationwide. The SIB plays a central role in life science research both in Switzerland and abroad by developing extensive and high-quality bioinformatics resources that are essential for all life scientists. Knowledge developed by SIB members in areas such as genomics, proteomics, and systems biology is directly transformed by academia and industry into innovative solutions to improve global health. Such an astounding concentration of talent in a given field is unusual and unique in Switzerland. This book provides an insight into some of the key areas of activity in bioinformatics in Switzerland. With contributions from SIB members, it covers both research work and major infrastructure efforts in genome and gene expression analysis, investigations on proteins and proteomes, evolutionary bioinformatics, and modeling of biological systems.

Probabilistic Methods for Financial and Marketing Informatics Book

Probabilistic Methods for Financial and Marketing Informatics


  • Author : Richard E. Neapolitan
  • Publisher : Elsevier
  • Release Date : 2010-07-26
  • Genre: Business & Economics
  • Pages : 432
  • ISBN 10 : 9780080555676
  • Total Read : 77
  • File Size : 20,9 Mb

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Probabilistic Methods for Financial and Marketing Informatics Summary:

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance Shares insights about when and why probabilistic methods can and cannot be used effectively Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.