3d Hidden Markov Models And Normal Mode Analysis For Modeling Protein Structure And Motion In A Statistical Framework
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3d Hidden Markov Models And Normal Mode Analysis For Modeling Protein Structure And Motion In A Statistical Framework
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Author : Vadim Alexandrov
language : en
Publisher:
Release Date : 2004
3d Hidden Markov Models And Normal Mode Analysis For Modeling Protein Structure And Motion In A Statistical Framework written by Vadim Alexandrov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Protein Modeling Using Hidden Markov Models
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Author :
language : en
Publisher:
Release Date : 1992
Protein Modeling Using Hidden Markov Models written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Markov processes categories.
Abstract: "We apply Hidden Markov Models (HMMs) to the problem of statistical modeling and multiple sequence alignment of protein families. A variant of the Expectation Maximization (EM) algorithm known as the Viterbi algorithm is used to obtain the statistical model from the unaligned sequences. In a detailed series of experiments, we have taken 400 unaligned globin sequences, and produced a statistical model entirely automatically from the primary (unaligned) sequences. We use no prior knowledge of globin structure. Using this model, we obtained a multiple alignment of the 400 sequences and 225 other globin sequences that agrees almost perfectly with a structural alignment by Bashford et al. This model can also discriminate all these 625 globins from nonglobin protein sequences with greater than 99% accuracy, and can thus be used for database searches."
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2005
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Dissertations, Academic categories.
Hidden Markov Models In Computational Biology
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Author : Anders Krogh
language : en
Publisher:
Release Date : 1993
Hidden Markov Models In Computational Biology written by Anders Krogh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Biological models categories.
Multiscale Approaches To Protein Modeling
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Author : Andrzej Kolinski
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-10-13
Multiscale Approaches To Protein Modeling written by Andrzej Kolinski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-13 with Science categories.
The book gives a comprehensive review of the most advanced multiscale methods for protein structure prediction, computational studies of protein dynamics, folding mechanisms and macromolecular interactions. It approaches span a wide range of the levels of coarse-grained representations, various sampling techniques and variety of applications to biomedical and biophysical problems. This book is intended to be used as a reference book for those who are just beginning their adventure with biomacromolecular modeling but also as a valuable source of detailed information for those who are already experts in the field of biomacromolecular modeling and in related areas of computational biology or biophysics.
Introduction To Hidden Semi Markov Models
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Author : John Van der Hoek
language : en
Publisher: Cambridge University Press
Release Date : 2018
Introduction To Hidden Semi Markov Models written by John Van der Hoek and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Hidden Markov models categories.
Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications
Protein Actions
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Author : Ken Dill
language : en
Publisher: Garland Science
Release Date : 2017-09-19
Protein Actions written by Ken Dill and has been published by Garland Science this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-19 with Medical categories.
Protein Actions: Principles and Modeling is aimed at graduates, advanced undergraduates, and any professional who seeks an introduction to the biological, chemical, and physical properties of proteins. Broadly accessible to biophysicists and biochemists, it will be particularly useful to student and professional structural biologists and molecular biophysicists, bioinformaticians and computational biologists, biological chemists (particularly drug designers) and molecular bioengineers. The book begins by introducing the basic principles of protein structure and function. Some readers will be familiar with aspects of this, but the authors build up a more quantitative approach than their competitors. Emphasizing concepts and theory rather than experimental techniques, the book shows how proteins can be analyzed using the disciplines of elementary statistical mechanics, energetics, and kinetics. These chapters illuminate how proteins attain biologically active states and the properties of those states. The book ends with a synopsis the roles of computational biology and bioinformatics in protein science.
Protein Structure Function Classification Using Hidden Markov Models
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Author : Moira Ellen Regelson
language : en
Publisher:
Release Date : 1997
Protein Structure Function Classification Using Hidden Markov Models written by Moira Ellen Regelson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Electronic dissertations categories.
Application Of Hidden Markov Model Based Methods For Gaining Insights Into Protein Domain Evolution And Function
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Author : Amit Anil Upadhyay
language : en
Publisher:
Release Date : 2015
Application Of Hidden Markov Model Based Methods For Gaining Insights Into Protein Domain Evolution And Function written by Amit Anil Upadhyay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Glycosidases categories.
With the explosion in the amount of available sequence data, computational methods have become indispensable for studying proteins. Domains are the fundamental structural, functional and evolutionary units that make up proteins. Studying protein domains is an important part of understanding protein function and evolution. Hidden Markov Models (HMM) are one of the most successful methods that have been applied for protein sequence and structure analysis. In this study, HMM based methods were applied to study the evolution of sensory domains in microbial signal transduction systems as well as functional characterization and identification of cellulases in metagenomics datasets. Use of HMM domain models enabled identification of the ambiguity in sequence and structure based definitions of the Cache domain family. Cache domains are extracellular sensory domains that are present in microbial signal transduction proteins and eukaryotic voltage gated calcium channels. The ambiguity in domain definitions was resolved and more accurate HMM models were built that detected more than 50,000 new members. It was discovered that Cache domains constitute the largest family of extracellular sensory domains in prokaryotes. Cache domains were also found to be remotely homologous to PAS domains at the level of sequence, a relationship previously suggested purely based on structural comparisons. We used HMM-HMM comparisons to study the diversity of extracellular sensory domains in prokaryotic signal transductions systems. This approach allowed annotation of more than 46,000 sequences and reduced the percentage of unknown domains from 64% to 15%. New relationships were also discovered between domain families that were otherwise thought to be unrelated. Finally, HMM models were used to retrieve Family 48 glycoside hydrolases (GH48) from sequence databases. Analysis of these sequences, enabled the identification of distinguishing features of cellulases. These features were used to identify GH48 cellulases from metagenomics datasets. In summary, HMM based methods enabled domain identification, remote homology detection and functional characterization of protein domains.
Hidden Markov Models With Applications In Computational Biology
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Author : Michael Seifert
language : en
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
Release Date : 2013-01
Hidden Markov Models With Applications In Computational Biology written by Michael Seifert and has been published by Sudwestdeutscher Verlag Fur Hochschulschriften AG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01 with categories.
Standard first-order Hidden Markov Models (HMMs) are very popular tools for the analysis of sequential data in applied sciences. HMMs are versatile and structurally simple models enabling probabilistic modeling based on a sound theoretical grounding. In contrast to the broad usage of first-order HMMs, applications of higher-order HMMs are very rare, but they have been proven to be powerful extensions of first-order HMMs including applications in speech recognition, image segmentation or computational biology. This book provides the first easily accessible and comprehensive extension of the algorithmic basics of first-order HMMs to higher-order HMMs coupled with practical applications in computational biology. The book starts with a theoretical part developing the algorithmic basics of higher-order HMMs and two novel model extensions (i) parsimonious higher-order HMMs and (ii) HMMs with scaled transition matrices. The second part considers applications of these models to the analysis of different DNA microarray data sets followed by a detailed discussion. The book addresses readers having basic knowledge on first-order HMMs interested to gain more insights on higher-order HMMs.