-PDF Download- Probabilistic Methods For Bioinformatics EBOOK

Probabilistic Methods for Bioinformatics

Author: Richard E. Neapolitan

Publisher: Morgan Kaufmann

Published: 2009-06-12

Total Pages: 421

ISBN-13: 0080919367

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

Author: Dirk Husmeier

Publisher: Springer Science & Business Media

Published: 2006-05-06

Total Pages: 511

ISBN-13: 1846281199

DOWNLOAD EBOOK →

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

Author: Richard Durbin

Publisher: Cambridge University Press

Published: 1998-04-23

Total Pages: 372

ISBN-13: 113945739X

DOWNLOAD EBOOK →

Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Bayesian Methods in Structural Bioinformatics

Author: Thomas Hamelryck

Publisher: Springer

Published: 2012-03-23

Total Pages: 399

ISBN-13: 3642272258

DOWNLOAD EBOOK →

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.

Probabilistic Methods for Financial and Marketing Informatics

Author: Richard E. Neapolitan

Publisher: Elsevier

Published: 2010-07-26

Total Pages: 427

ISBN-13: 0080555675

DOWNLOAD EBOOK →

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.

Statistical Methods in Bioinformatics

Author: Warren J. Ewens

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 485

ISBN-13: 1475732473

DOWNLOAD EBOOK →

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.

Probabilistic Boolean Networks

Author: Ilya Shmulevich

Publisher: SIAM

Published: 2010-01-21

Total Pages: 276

ISBN-13: 0898716926

DOWNLOAD EBOOK →

The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

Modeling the Internet and the Web

Author: Pierre Baldi

Publisher: John Wiley & Sons

Published: 2003-07-07

Total Pages: 320

ISBN-13:

DOWNLOAD EBOOK →

Despite its haphazard growth, the Web hides powerful underlying regularities - from the organization of its links to the patterns found in its use by millions of users. Probabilistic modelling allows many of these regularities to be predicted on the basis of theoretical models based on statistical methodology.

Data Analysis and Classification for Bioinformatics

Author: Arun Jagota

Publisher:

Published: 2000

Total Pages: 98

ISBN-13:

DOWNLOAD EBOOK →

Probability theory. Probability distributions. Tests of statistical significance. Information theory. Clustering methods. Probability models. The supervised classification problem. Probabilistic classifers. Neural networks. Decision trees. Nearest neighbor classifers.

Bioinformatics

Author: Pierre Baldi

Publisher: MIT Press (MA)

Published: 1998

Total Pages: 351

ISBN-13: 9780262024426

DOWNLOAD EBOOK →

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, 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, or computer science who need to know more about specific applications in molecular biology.