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Hidden Markov Models

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Cover of 'Hidden Markov Models'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Hidden Markov Models and Its Applications in Biology
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    Chapter 2 HMMs in Protein Fold Classification
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    Chapter 3 Application of Hidden Markov Models in Biomolecular Simulations
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    Chapter 4 Predicting Beta Barrel Transmembrane Proteins Using HMMs
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    Chapter 5 Predicting Alpha Helical Transmembrane Proteins Using HMMs
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    Chapter 6 Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization
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    Chapter 7 Analyzing Single Molecule FRET Trajectories Using HMM
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    Chapter 8 Modelling ChIP-seq Data Using HMMs
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    Chapter 9 Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence
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    Chapter 10 Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies
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    Chapter 11 Hidden Markov Models in Population Genomics
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    Chapter 12 Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches
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    Chapter 13 Finding RNA–Protein Interaction Sites Using HMMs
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    Chapter 14 Automated Estimation of Mouse Social Behaviors Based on a Hidden Markov Model
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    Chapter 15 Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications
Attention for Chapter 6: Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization
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Chapter title
Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization
Chapter number 6
Book title
Hidden Markov Models
Published in
Methods in molecular biology, February 2017
DOI 10.1007/978-1-4939-6753-7_6
Pubmed ID
28224492
Book ISBNs
978-1-4939-6751-3, 978-1-4939-6753-7
Authors

Christos Ferles, William-Scott Beaufort, Vanessa Ferle

Editors

David R. Westhead, M. S. Vijayabaskar

Abstract

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 50%
Researcher 1 25%
Student > Bachelor 1 25%
Readers by discipline Count As %
Engineering 2 50%
Neuroscience 1 25%
Nursing and Health Professions 1 25%

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