Probabilistic Modeling In Bioinformatics And Medical Informatics

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Probabilistic Modeling in Bioinformatics and Medical Informatics

Dirk Husmeier,Richard Dybowski,Stephen Roberts
Probabilistic Modeling in Bioinformatics and Medical Informatics Book PDF

Author : Dirk Husmeier,Richard Dybowski,Stephen Roberts
Publisher : Springer Science & Business Media
Page : 511 pages
File Size : 54,7 Mb
Release : 2006-05-06
Category : Computers
ISBN : 9781846281198

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Probabilistic Modeling in Bioinformatics and Medical Informatics by Dirk Husmeier,Richard Dybowski,Stephen Roberts Pdf

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.

Handbook of Statistical Bioinformatics

Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao
Handbook of Statistical Bioinformatics Book PDF

Author : Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao
Publisher : Springer Nature
Page : 406 pages
File Size : 48,5 Mb
Release : 2022-12-08
Category : Science
ISBN : 9783662659021

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Handbook of Statistical Bioinformatics by Henry Horng-Shing Lu,Bernhard Schölkopf,Martin T. Wells,Hongyu Zhao Pdf

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Supply Chain Management: Concepts, Methodologies, Tools, and Applications

Management Association, Information Resources
Supply Chain Management Concepts Methodologies Tools and Applications Book PDF

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 1838 pages
File Size : 47,7 Mb
Release : 2012-12-31
Category : Business & Economics
ISBN : 9781466626751

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Supply Chain Management: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf

In order to keep up with the constant changes in technology, business have adopted supply chain management to improve competitive strategies on a strategic and operational level. Supply Chain Management: Concepts, Methodologies, Tools, and Applications is a reference collection which highlights the major concepts and issues in the application and advancement of supply chain management. Including research from leading scholars, this resource will be useful for academics, students, and practitioners interested in the continuous study of supply chain management and its influences.

Artificial Intelligence in Medicine

David Riaño,Szymon Wilk,Annette ten Teije
Artificial Intelligence in Medicine Book PDF

Author : David Riaño,Szymon Wilk,Annette ten Teije
Publisher : Springer
Page : 431 pages
File Size : 53,6 Mb
Release : 2019-06-19
Category : Computers
ISBN : 9783030216429

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Artificial Intelligence in Medicine by David Riaño,Szymon Wilk,Annette ten Teije Pdf

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

Machine Learning, Big Data, and IoT for Medical Informatics

Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid
Machine Learning Big Data and IoT for Medical Informatics Book PDF

Author : Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid
Publisher : Academic Press
Page : 458 pages
File Size : 49,5 Mb
Release : 2021-06-13
Category : Computers
ISBN : 9780128217818

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Machine Learning, Big Data, and IoT for Medical Informatics by Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid Pdf

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

Handbook of Statistical Systems Biology

Michael Stumpf,David J. Balding,Mark Girolami
Handbook of Statistical Systems Biology Book PDF

Author : Michael Stumpf,David J. Balding,Mark Girolami
Publisher : John Wiley & Sons
Page : 624 pages
File Size : 51,6 Mb
Release : 2011-09-09
Category : Science
ISBN : 9781119952046

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Handbook of Statistical Systems Biology by Michael Stumpf,David J. Balding,Mark Girolami Pdf

Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Hybrid Artificial Intelligent Systems

Francisco Javier de Cos Juez,José Ramón Villar,Enrique A. de la Cal,Álvaro Herrero,Héctor Quintián,José António Sáez,Emilio Corchado
Hybrid Artificial Intelligent Systems Book PDF

Author : Francisco Javier de Cos Juez,José Ramón Villar,Enrique A. de la Cal,Álvaro Herrero,Héctor Quintián,José António Sáez,Emilio Corchado
Publisher : Springer
Page : 765 pages
File Size : 52,6 Mb
Release : 2018-06-09
Category : Computers
ISBN : 9783319926391

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Hybrid Artificial Intelligent Systems by Francisco Javier de Cos Juez,José Ramón Villar,Enrique A. de la Cal,Álvaro Herrero,Héctor Quintián,José António Sáez,Emilio Corchado Pdf

This volume constitutes the refereed proceedings of the 13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018, held in Oviedo, Spain, in June 2018. The 62 full papers published in this volume were carefully reviewed and selected from 104 submissions. They are organized in the following topical sections: Neurocomputing, fuzzy systems, rough sets, evolutionary algorithms, Agents andMultiagent Systems, and alike.

Advances in Computers

Anonim
Advances in Computers Book PDF

Author : Anonim
Publisher : Academic Press
Page : 276 pages
File Size : 53,7 Mb
Release : 2015-02-28
Category : Computers
ISBN : 9780128023419

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Advances in Computers by Anonim Pdf

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field. In-depth surveys and tutorials on new computer technology Well-known authors and researchers in the field Extensive bibliographies with most chapters Many of the volumes are devoted to single themes or subfields of computer science

Probabilistic Methods for Bioinformatics

Richard E. Neapolitan
Probabilistic Methods for Bioinformatics Book PDF

Author : Richard E. Neapolitan
Publisher : Morgan Kaufmann
Page : 424 pages
File Size : 45,8 Mb
Release : 2009-06-12
Category : Computers
ISBN : 0080919367

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Probabilistic Methods for Bioinformatics by Richard E. Neapolitan Pdf

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.

Pattern Recognition in Computational Molecular Biology

Mourad Elloumi,Costas Iliopoulos,Jason T. L. Wang,Albert Y. Zomaya
Pattern Recognition in Computational Molecular Biology Book PDF

Author : Mourad Elloumi,Costas Iliopoulos,Jason T. L. Wang,Albert Y. Zomaya
Publisher : John Wiley & Sons
Page : 656 pages
File Size : 50,6 Mb
Release : 2015-12-24
Category : Technology & Engineering
ISBN : 9781119078869

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Pattern Recognition in Computational Molecular Biology by Mourad Elloumi,Costas Iliopoulos,Jason T. L. Wang,Albert Y. Zomaya Pdf

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

Hidden Markov Models for Bioinformatics

T. Koski
Hidden Markov Models for Bioinformatics Book PDF

Author : T. Koski
Publisher : Springer Science & Business Media
Page : 422 pages
File Size : 46,7 Mb
Release : 2001-11-30
Category : Computers
ISBN : 1402001355

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Hidden Markov Models for Bioinformatics by T. Koski Pdf

The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis.

Statistical Methods in Bioinformatics

Warren J. Ewens,Gregory R. Grant
Statistical Methods in Bioinformatics Book PDF

Author : Warren J. Ewens,Gregory R. Grant
Publisher : Springer Science & Business Media
Page : 598 pages
File Size : 44,7 Mb
Release : 2006-03-30
Category : Science
ISBN : 9780387266480

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Statistical Methods in Bioinformatics by Warren J. Ewens,Gregory R. Grant Pdf

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)

Biological Sequence Analysis

Richard Durbin
Biological Sequence Analysis Book PDF

Author : Richard Durbin
Publisher : Unknown
Page : 0 pages
File Size : 47,5 Mb
Release : 1998
Category : Amino acid sequence
ISBN : 0511337086

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Biological Sequence Analysis by Richard Durbin Pdf

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

Bayesian Modeling in Bioinformatics

Dipak K. Dey,Samiran Ghosh,Bani K. Mallick
Bayesian Modeling in Bioinformatics Book PDF

Author : Dipak K. Dey,Samiran Ghosh,Bani K. Mallick
Publisher : CRC Press
Page : 466 pages
File Size : 40,5 Mb
Release : 2010-09-03
Category : Mathematics
ISBN : 9781420070187

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Bayesian Modeling in Bioinformatics by Dipak K. Dey,Samiran Ghosh,Bani K. Mallick Pdf

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c

Analyzing Data Through Probabilistic Modeling in Statistics

Jakóbczak, Dariusz Jacek
Analyzing Data Through Probabilistic Modeling in Statistics Book PDF

Author : Jakóbczak, Dariusz Jacek
Publisher : IGI Global
Page : 331 pages
File Size : 43,7 Mb
Release : 2021-02-19
Category : Mathematics
ISBN : 9781799847076

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Analyzing Data Through Probabilistic Modeling in Statistics by Jakóbczak, Dariusz Jacek Pdf

Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic modeling meets statistics, it is a very interesting occasion that has gained much research recently. With the increase of these technologies in life and work, it has become somewhat essential in the workplace to have planning, timetabling, scheduling, decision making, optimization, simulation, data analysis, and risk analysis and process modeling. However, there are still many difficulties and challenges that arrive in these sectors during the process of planning or decision making. There continues to be the need for more research on the impact of such probabilistic modeling with other approaches. Analyzing Data Through Probabilistic Modeling in Statistics is an essential reference source that builds on the available literature in the field of probabilistic modeling, statistics, operational research, planning and scheduling, data extrapolation in decision making, probabilistic interpolation and extrapolation in simulation, stochastic processes, and decision analysis. This text will provide the resources necessary for economics and management sciences and for mathematics and computer sciences. This book is ideal for interested technology developers, decision makers, mathematicians, statisticians and practitioners, stakeholders, researchers, academicians, and students looking to further their research exposure to pertinent topics in operations research and probabilistic modeling.