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 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.

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  • Author : Richard E. Neapolitan
  • Publisher : Morgan Kaufmann
  • Pages : 424 pages
  • ISBN : 9780080919362
  • Rating : 1/5 from 1 reviews
CLICK HERE TO GET THIS BOOKProbabilistic Methods for Bioinformatics

Probabilistic Methods for Bioinformatics

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

Artificial Intelligence

Artificial Intelligence
  • Author : Richard E. Neapolitan,Xia Jiang
  • Publisher : CRC Press
  • Release : 12 March 2018
GET THIS BOOKArtificial Intelligence

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : Warren J. Ewens,Gregory R. Grant
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOKStatistical Methods in Bioinformatics

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

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 BOOKComputational 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

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 BOOKBayesian 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

Machine Learning in Bioinformatics

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

Statistical Bioinformatics

Statistical Bioinformatics
  • Author : Jae K. Lee
  • Publisher : John Wiley & Sons
  • Release : 20 September 2011
GET THIS BOOKStatistical Bioinformatics

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

Bioinformatics

Bioinformatics
  • Author : Pierre Baldi,Professor Pierre Baldi,Søren Brunak,Francis Bach
  • Publisher : MIT Press
  • Release : 18 May 2021
GET THIS BOOKBioinformatics

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics,

Contemporary Artificial Intelligence

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

Bioinformatics Algorithms

Bioinformatics Algorithms
  • Author : Miguel Rocha,Pedro G. Ferreira
  • Publisher : Academic Press
  • Release : 08 June 2018
GET THIS BOOKBioinformatics Algorithms

Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology
  • Author : Hamid R Arabnia,Quoc Nam Tran
  • Publisher : Morgan Kaufmann
  • Release : 25 March 2016
GET THIS BOOKEmerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field with special emphasis on their applications. The first part covers the major areas of computational biology, development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques for the study of biological and behavioral systems. The second part covers bioinformatics, an interdisciplinary field concerned with methods for storing, retrieving, organizing, and analyzing biological data.

Fundamentals of Bioinformatics and Computational Biology

Fundamentals of Bioinformatics and Computational Biology
  • Author : Gautam B. Singh
  • Publisher : Springer
  • Release : 24 September 2014
GET THIS BOOKFundamentals of Bioinformatics and Computational Biology

This book offers comprehensive coverage of all the core topics of bioinformatics, and includes practical examples completed using the MATLAB bioinformatics toolboxTM. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics. This chapter will enable physical science students to fully understand and appreciate the ultimate goals