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Probabilistic Methods for Bioinformatics

Probabilistic Methods for Bioinformatics
with an Introduction to Bayesian Networks

by Richard E. Neapolitan

  • Publisher : Morgan Kaufmann
  • Release : 2009-06-12
  • Pages : 424
  • ISBN : 9780080919362
  • Language : En, Es, Fr & De
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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

Probabilistic Modeling in Bioinformatics and Medical Informatics
A Book

by Dirk Husmeier,Richard Dybowski,Stephen Roberts

  • Publisher : Springer Science & Business Media
  • Release : 2006-03-30
  • Pages : 508
  • ISBN : 1846281199
  • Language : En, Es, Fr & De
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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

Biological Sequence Analysis
Probabilistic Models of Proteins and Nucleic Acids

by Richard Durbin,Sean R. Eddy,Anders Krogh,Sean Eddy,Graeme Mitchison

  • Publisher : Cambridge University Press
  • Release : 1998-04-23
  • Pages : 356
  • ISBN : 9780521620413
  • Language : En, Es, Fr & De
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Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
An Introduction

by Warren J. Ewens,Gregory R. Grant

  • Publisher : Springer Science & Business Media
  • Release : 2006-03-30
  • Pages : 598
  • ISBN : 0387266488
  • Language : En, Es, Fr & De
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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)

Bayesian Methods in Structural Bioinformatics

Bayesian Methods in Structural Bioinformatics
A Book

by Thomas Hamelryck,Kanti Mardia,Jesper Ferkinghoff-Borg

  • Publisher : Springer
  • Release : 2012-03-23
  • Pages : 386
  • ISBN : 3642272258
  • Language : En, Es, Fr & De
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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.

Modeling the Internet and the Web

Modeling the Internet and the Web
Probabilistic Methods and Algorithms

by Pierre Baldi,Paolo Frasconi,Padhraic Smyth

  • Publisher : Wiley-Blackwell
  • Release : 2003-07-07
  • Pages : 285
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
A Book

by Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis

  • Publisher : CRC Press
  • Release : 2008-06-05
  • Pages : 384
  • ISBN : 1420011782
  • Language : En, Es, Fr & De
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Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Bioinformatics

Bioinformatics
The Machine Learning Approach

by Pierre Baldi,Professor Pierre Baldi,Søren Brunak,Francis Bach

  • Publisher : MIT Press
  • Release : 2001
  • Pages : 452
  • ISBN : 9780262025065
  • Language : En, Es, Fr & De
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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.

Artificial Intelligence

Artificial Intelligence
With an Introduction to Machine Learning, Second Edition

by Richard E. Neapolitan,Xia Jiang

  • Publisher : CRC Press
  • Release : 2018-03-12
  • Pages : 466
  • ISBN : 1351384392
  • Language : En, Es, Fr & De
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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 focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.

Foundations of Algorithms

Foundations of Algorithms
A Book

by Richard Neapolitan,Kumarss Naimipour

  • Publisher : Jones & Bartlett Publishers
  • Release : 2010-04-01
  • Pages : 627
  • ISBN : 1449600166
  • Language : En, Es, Fr & De
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Foundations of Algorithms, Fourth Edition offers a well-balanced presentation of algorithm design, complexity analysis of algorithms, and computational complexity. The volume is accessible to mainstream computer science students who have a background in college algebra and discrete structures. To support their approach, the authors present mathematical concepts using standard English and a simpler notation than is found in most texts. A review of essential mathematical concepts is presented in three appendices. The authors also reinforce the explanations with numerous concrete examples to help students grasp theoretical concepts.

Bayesian Methods in Structural Bioinformatics

Bayesian Methods in Structural Bioinformatics
A Book

by Thomas Hamelryck,Kanti Mardia,Jesper Ferkinghoff-Borg

  • Publisher : Springer
  • Release : 2012-03-23
  • Pages : 386
  • ISBN : 3642272258
  • Language : En, Es, Fr & De
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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.

Contemporary Artificial Intelligence

Contemporary Artificial Intelligence
A Book

by Richard E. Neapolitan

  • Publisher : CRC Press
  • Release : 2012-08-23
  • Pages : 515
  • ISBN : 1466559403
  • Language : En, Es, Fr & De
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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 such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more. One of the first AI texts accessible to students, the book focuses on the most useful problem-solving strategies that have emerged from AI. In a student-friendly way, the authors cover logic-based methods; probability-based methods; emergent intelligence, including evolutionary computation and swarm intelligence; data-derived logical and probabilistic learning models; and natural language understanding. Through reading this book, students discover the importance of AI techniques in computer science.

Fundamentals of Bioinformatics and Computational Biology

Fundamentals of Bioinformatics and Computational Biology
Methods and Exercises in MATLAB

by Gautam B. Singh

  • Publisher : Springer
  • Release : 2014-09-24
  • Pages : 339
  • ISBN : 3319114034
  • Language : En, Es, Fr & De
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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 of applying the principles of information technology to challenges in biological data management, sequence analysis, and systems biology. The first part of the book also includes a survey of existing biological databases, tools that have become essential in today’s biotechnology research. The second part of the book covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The main focus of the third part is on modeling biological sequences and patterns as Markov chains. It presents key principles for analyzing and searching for sequences of significant motifs and biomarkers. The last part of the book, dedicated to systems biology, covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In brief, the book offers the ideal hands-on reference guide to the field of bioinformatics and computational biology.

Bioinformatics

Bioinformatics
A Swiss Perspective

by Ron D. Appel,Ernest Feytmans

  • Publisher : World Scientific
  • Release : 2009
  • Pages : 443
  • ISBN : 9812838775
  • Language : En, Es, Fr & De
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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.

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
A Book

by Yanqing Zhang,Jagath C. Rajapakse

  • Publisher : John Wiley & Sons
  • Release : 2009-02-23
  • Pages : 400
  • ISBN : 9780470397411
  • Language : En, Es, Fr & De
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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 data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

Computational Intelligence Methods for Bioinformatics and Biostatistics

Computational Intelligence Methods for Bioinformatics and Biostatistics
15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018, Revised Selected Papers

by Maria Raposo,Paulo Ribeiro,Susana Sério,Antonino Staiano,Angelo Ciaramella

  • Publisher : Springer Nature
  • Release : 2020-01-22
  • Pages : 334
  • ISBN : 3030345858
  • Language : En, Es, Fr & De
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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 and fostered multidisciplinary collaboration aiming to blend theory and practice, where the founding theories of the techniques used for modelling and analyzing biological systems are investigated and used for practical applications and the supporting technologies.

Bioinformatics Research and Applications

Bioinformatics Research and Applications
Fourth International Symposium, ISBRA 2008, Atlanta, GA, USA, May 6-9, 2008, Proceedings

by Ion Măndoiu,Rajshekhar Sunderraman,Alexander Zelikovsky

  • Publisher : Springer Science & Business Media
  • Release : 2008-04-25
  • Pages : 510
  • ISBN : 3540794492
  • Language : En, Es, Fr & De
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th The 4 edition of the InternationalSymposium on BioinformaticsResearchand Applications (ISBRA 2008) was held on May 6-9, 2008 at Georgia State U- versity in Atlanta, Georgia. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. The technical program of the symposium included 35 contributed papers, selected by the Program Committee from a number of 94 full submissions - ceived in response to the call for papers. The technical program also included six papers contributed to the First International Workshop on Optimal Data Mining in Gene Expression Analysis (ODGEA 2008), which was held in c- junction with ISBRA 2008. In addition to the contributed papers, the sym- sium included tutorials and poster sessions and featured invited keynote talks by six distinguished speakers. Andrew Scott Allen from Duke University and Dan Nicolae from the University of Chicago spoke on novel analysis methods for genome-wide association studies; Kenneth Buetow, director of the National Cancer Institute Center for Bioinformatics, spoke on the cancer Biomedical - formatics Grid; Andrey Gorin from Oak Ridge National Laboratory spoke on peptide identi?cation from mass spectrometry data; Yury Khudyakov from the Center for Disease Control and Prevention spoke on integrative viral molecular epidemiology; and Kwok Tsui from Georgia Institute of Technology spoke on data mining and statistical methods for analyzing microarray experiments.

Probabilistic Graphical Models for Genetics, Genomics and Postgenomics

Probabilistic Graphical Models for Genetics, Genomics and Postgenomics
A Book

by Christine Sinoquet

  • Publisher : Oxford University Press, USA
  • Release : 2014
  • Pages : 449
  • ISBN : 0198709021
  • Language : En, Es, Fr & De
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At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. For instance, Bayesian networks and Markov random fields are two of the most popular probabilistic graphical models. With the rapid advance of high-throughput technologies and their ever decreasing costs, a fast-growing volume of biological data of various types - the so-called ''omics'' - is in need of accurate andefficient methods for modeling, prior to further downstream analysis. As probabilistic graphical models are able to deal with high-dimensional data, it is foreseeable that such models will have aprominent role to play in advances in genome-wide data analyses. Currently, few people are specialists in the design of cutting-edge methods using probabilistic graphical models for genetics, genomics and postgenomics. This seriously hinders the diffusion of such methods. The prime aim of the book is therefore to bring the concepts underlying these advanced models within reach of scientists who are not specialists of these models, but with no concession on theinformativeness of the book. The target readers include researchers and engineers who have to design novel methods for postgenomics data analysis, as well as graduate students starting a Masters or a PhD. Inaddition to an introductory chapter on probabilistic graphical models, a thorough review chapter focusing on selected domains in genetics and fourteen chapters illustrate the design of such advanced approaches in various domains: gene network inference, inference of causal phenotype networks, association genetics, epigenetics, detection of copy number variations, and prediction of outcomes from high-dimensional genomic data. Notably, most examples also illustrate that probabilistic graphicalmodels are well suited for integrative biology and systems biology, hot topics guaranteed to be of lasting interest.

Bioinformatics and Molecular Evolution

Bioinformatics and Molecular Evolution
A Book

by Paul G. Higgs,Teresa K. Attwood

  • Publisher : John Wiley & Sons
  • Release : 2013-04-30
  • Pages : 384
  • ISBN : 1118697065
  • Language : En, Es, Fr & De
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In the current era of complete genome sequencing, Bioinformatics and Molecular Evolution provides an up-to-date and comprehensive introduction to bioinformatics in the context of evolutionary biology. This accessible text: provides a thorough examination of sequence analysis, biological databases, pattern recognition, and applications to genomics, microarrays, and proteomics emphasizes the theoretical and statistical methods used in bioinformatics programs in a way that is accessible to biological science students places bioinformatics in the context of evolutionary biology, including population genetics, molecular evolution, molecular phylogenetics, and their applications features end-of-chapter problems and self-tests to help students synthesize the materials and apply their understanding is accompanied by a dedicated website - www.blackwellpublishing.com/higgs - containing downloadable sequences, links to web resources, answers to self-test questions, and all artwork in downloadable format (artwork also available to instructors on CD-ROM). This important textbook will equip readers with a thorough understanding of the quantitative methods used in the analysis of molecular evolution, and will be essential reading for advanced undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics courses.

Statistical Bioinformatics

Statistical Bioinformatics
For Biomedical and Life Science Researchers

by Jae K. Lee

  • Publisher : John Wiley & Sons
  • Release : 2011-09-20
  • Pages : 364
  • ISBN : 1118211529
  • Language : En, Es, Fr & De
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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.