Probabilistic Methods for Bioinformatics

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  • Author : Richard E. Neapolitan
  • Publisher : Morgan Kaufmann
  • Pages : 424 pages
  • ISBN : 9780080919362
  • Rating : 1/5 from 1 reviews
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Download or Read online Probabilistic Methods for Bioinformatics full in PDF, ePub and kindle. this book written by Richard E. Neapolitan and published by Morgan Kaufmann which was released on 12 June 2009 with total page 424 pages. We cannot guarantee that Probabilistic Methods for Bioinformatics book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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 Bioinformatics

Probabilistic Methods for Bioinformatics
  • Author : Richard E. Neapolitan
  • Publisher : Morgan Kaufmann
  • Release : 12 June 2009
GET THIS BOOK Probabilistic Methods for Bioinformatics

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

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics
  • Author : Dirk Husmeier,Richard Dybowski,Stephen Roberts
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOK Probabilistic Modeling in Bioinformatics and Medical Informatics

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

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : Warren J. Ewens,Gregory R. Grant
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOK Statistical Methods in Bioinformatics

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.

Theory and Mathematical Methods in Bioinformatics

Theory and Mathematical Methods in Bioinformatics
  • Author : Shiyi Shen
  • Publisher : Springer Science & Business Media
  • Release : 26 January 2008
GET THIS BOOK Theory and Mathematical Methods in Bioinformatics

This monograph addresses, in a systematic and pedagogical manner, the mathematical methods and the algorithms required to deal with the molecularly based problems of bioinformatics. Prominent attention is given to pair-wise and multiple sequence alignment algorithms, stochastic models of mutations, modulus structure theory and protein configuration analysis. Strong links to the molecular structures of proteins, DNA and other biomolecules and their analyses are developed.

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
  • Author : Yanqing Zhang,Jagath C. Rajapakse
  • Publisher : John Wiley & Sons
  • Release : 23 February 2009
GET THIS BOOK Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science

Contemporary Artificial Intelligence

Contemporary Artificial Intelligence
  • Author : Richard E. Neapolitan
  • Publisher : CRC Press
  • Release : 23 August 2012
GET THIS BOOK Contemporary Artificial Intelligence

The notion of artificial intelligence (AI) often sparks thoughts of characters from science fiction, such as the Terminator and HAL 9000. While these two artificial entities do not exist, the algorithms of AI have been able to address many real issues, from performing medical diagnoses to navigating difficult terrain to monitoring possible failures of spacecrafts. Exploring these algorithms and applications, Contemporary Artificial Intelligence presents strong AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains

Bayesian Methods in Structural Bioinformatics

Bayesian Methods in Structural Bioinformatics
  • Author : Thomas Hamelryck,Kanti Mardia,Jesper Ferkinghoff-Borg
  • Publisher : Springer
  • Release : 23 March 2012
GET THIS BOOK Bayesian Methods in Structural Bioinformatics

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

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : W. Warren John Ewens,Gregory Robert Grant
  • Publisher : Springer Science & Business Media
  • Release : 28 September 2021
GET THIS BOOK Statistical Methods in Bioinformatics

Probability theory (i): one random variable. Probability theory (ii); many random variables. Statistics (i): an introduction to statistical inference. Stochastic processes (i): poisson processes and markov chains. Stochastic processes (iii): markov chains. Hidden markov models. Computationally intensive methods. Evolutionary models. Phylogenetic tree estimation. Basic notions in biology. C computational aspects of the binominal and generalized geometric distribution functions. D BLAST: sums of normalized scores. References. Author index. Index.

Introduction to Mathematical Methods in Bioinformatics

Introduction to Mathematical Methods in Bioinformatics
  • Author : Alexander Isaev
  • Publisher : Springer
  • Release : 04 October 2006
GET THIS BOOK Introduction to Mathematical Methods in Bioinformatics

This book looks at the mathematical foundations of the models currently in use. All existing books on bioinformatics are software-orientated and they concentrate on computer implementations of mathematical models of biology. This book is unique in the sense that it looks at the mathematical foundations of the models, which are crucial for correct interpretation of the outputs of the models.

Computational Intelligence Methods for Bioinformatics and Biostatistics

Computational Intelligence Methods for Bioinformatics and Biostatistics
  • Author : Maria Raposo,Paulo Ribeiro,Susana Sério,Antonino Staiano,Angelo Ciaramella
  • Publisher : Springer Nature
  • Release : 22 January 2020
GET THIS BOOK Computational Intelligence Methods for Bioinformatics and Biostatistics

This book constitutes the thoroughly refereed post-conference proceedings of the 15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics., CIBB 2018, held in Caparica, Portugal, in September 2018. The 32 revised full papers were carefully reviewed and selected from 51 submissions. The papers present current trends at the edge of computer and life sciences, the application of computational intelligence to a system and synthetic biology and the consequent impact on innovative medicine were presented. Theoretical and experimental biologists also presented novel challenges

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics
  • Author : Richard Dybowski,Dirk Husmeier,Stephen Roberts
  • Publisher : Unknown
  • Release : 28 September 2021
GET THIS BOOK Probabilistic Modeling in Bioinformatics and Medical Informatics

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

Computational Intelligence Methods for Bioinformatics and Biostatistics

Computational Intelligence Methods for Bioinformatics and Biostatistics
  • Author : Riccardo Rizzo,Paulo J.G. Lisboa
  • Publisher : Springer
  • Release : 18 July 2011
GET THIS BOOK Computational Intelligence Methods for Bioinformatics and Biostatistics

This book constitutes the thoroughly refereed post-proceedings of the 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010, held in Palermo, Italy, in September 2010. The 19 papers, presented together with 2 keynote speeches and 1 tutorial, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on sequence analysis, promoter analysis and identification of transcription factor binding sites; methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- prediction of secondary

Bioinformatics

Bioinformatics
  • Author : Yu Liu
  • Publisher : CRC Press
  • Release : 24 February 2014
GET THIS BOOK Bioinformatics

This title includes a number of Open Access chapters. The book introduces bioinformatic and statistical methodology and shows approaches to bias correction and error estimation. It also presents quantitative methods for genome and proteome analysis.

Introduction to Mathematical Methods in Bioinformatics

Introduction to Mathematical Methods in Bioinformatics
  • Author : Alexander Isaev
  • Publisher : Springer Science & Business Media
  • Release : 19 September 2006
GET THIS BOOK Introduction to Mathematical Methods in Bioinformatics

This book looks at the mathematical foundations of the models currently in use. All existing books on bioinformatics are software-orientated and they concentrate on computer implementations of mathematical models of biology. This book is unique in the sense that it looks at the mathematical foundations of the models, which are crucial for correct interpretation of the outputs of the models.