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Hidden Markov Models

Methods and Protocols

  • Book
  • © 2017

Overview

Editors:
  1. David R. Westhead

    1. University of Leeds School of Molecular and Cellular Biology, Leeds, United Kingdom

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    You can also search for this editor in PubMed   Google Scholar

  2. M. S. Vijayabaskar

    1. University of Leeds School of Cellular and Molecular Biology, Leeds, United Kingdom

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 1552)

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Table of contents (15 protocols)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Introduction to Hidden Markov Models and Its Applications in Biology

    • M. S. Vijayabaskar
    Pages 1-12

  3. HMMs in Protein Fold Classification

    • Christos Lampros, Costas Papaloukas, Themis Exarchos, Dimitrios I. Fotiadis
    Pages 13-27

  4. Application of Hidden Markov Models in Biomolecular Simulations

    • Saurabh Shukla, Zahra Shamsi, Alexander S. Moffett, Balaji Selvam, Diwakar Shukla
    Pages 29-41

  5. Predicting Beta Barrel Transmembrane Proteins Using HMMs

    • Georgios N. Tsaousis, Stavros J. Hamodrakas, Pantelis G. Bagos
    Pages 43-61

  6. Predicting Alpha Helical Transmembrane Proteins Using HMMs

    • Georgios N. Tsaousis, Margarita C. Theodoropoulou, Stavros J. Hamodrakas, Pantelis G. Bagos
    Pages 63-82

  7. Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization

    • Christos Ferles, William-Scott Beaufort, Vanessa Ferle
    Pages 83-101

  8. Analyzing Single Molecule FRET Trajectories Using HMM

    • Kenji Okamoto
    Pages 103-113

  9. Modelling ChIP-seq Data Using HMMs

    • Veronica Vinciotti
    Pages 115-122

  10. Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence

    • Jiawen Bian, Xiaobo Zhou
    Pages 123-133

  11. Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies

    • Hyungwon Choi, Debashis Ghosh, Zhaohui Qin
    Pages 135-148

  12. Hidden Markov Models in Population Genomics

    • Julien Y. Dutheil
    Pages 149-164

  13. Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches

    • Sunghee Oh, Seongho Song
    Pages 165-176

  14. Finding RNA–Protein Interaction Sites Using HMMs

    • Tao Wang, Jonghyun Yun, Yang Xie, Guanghua Xiao
    Pages 177-184

  15. Automated Estimation of Mouse Social Behaviors Based on a Hidden Markov Model

    • Toshiya Arakawa, Akira Tanave, Aki Takahashi, Satoshi Kakihara, Tsuyoshi Koide, Takashi Tsuchiya
    Pages 185-197

  16. Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications

    • Michelle Karg, Dana Kulić
    Pages 199-213

  17. Back Matter

    Pages 215-221
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Keywords

About this book

This volume aims to provide a new perspective on the broader usage of Hidden Markov Models (HMMs) in biology. Hidden Markov Models: Methods and Protocols guides readers through chapters on biological systems; ranging from single biomolecule, cellular level, and to organism level and the use of HMMs in unravelling the complex mechanisms that govern these complex systems.  Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and practical, Hidden Markov Models: Methods and Protocols aims to demonstrate the impact of HMM in biology and inspire new research.

Editors and Affiliations

  • University of Leeds School of Molecular and Cellular Biology, Leeds, United Kingdom

    David R. Westhead

  • University of Leeds School of Cellular and Molecular Biology, Leeds, United Kingdom

    M. S. Vijayabaskar

Bibliographic Information

  • Book Title: Hidden Markov Models

  • Book Subtitle: Methods and Protocols

  • Editors: David R. Westhead, M. S. Vijayabaskar

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-6753-7

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media LLC 2017

  • Hardcover ISBN: 978-1-4939-6751-3Published: 22 February 2017

  • Softcover ISBN: 978-1-4939-8292-9Published: 12 July 2018

  • eBook ISBN: 978-1-4939-6753-7Published: 21 February 2017

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 221

  • Number of Illustrations: 42 b/w illustrations, 17 illustrations in colour

  • Topics: Bioinformatics

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