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OverviewThis 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. Full Product DetailsAuthor: David R. Westhead , M. S. VijayabaskarPublisher: Humana Press Inc. Imprint: Humana Press Inc. Edition: 1st ed. 2017 Volume: 1552 Dimensions: Width: 17.80cm , Height: 1.40cm , Length: 25.40cm Weight: 5.855kg ISBN: 9781493967513ISBN 10: 1493967517 Pages: 221 Publication Date: 22 February 2017 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsIntroduction to Hidden Markov Models and its Applications in Biology.- HMMs in Protein Fold Classification.- Application of Hidden Markov Models in Biomolecular Simulations.- Predicting Beta Barrel Transmembrane Proteins using HMMs.- Predicting Alpha Helical Transmembrane Proteins using HMMs.- Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization.- Analyzing Single Molecule FRET Trajectories using HMM.- Modelling ChIP-seq Data using HMMs.- Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence.- Computationally Tractable Multivariate HMM in Genome-wide Mapping Studies.- Hidden Markov Models in Population Genomics.- Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes using HMMs and Hierarchical Bayesian Modeling Approaches.- Finding RNA-Protein Interaction Sites using HMM.- Automated Estimation of Mouse Social Behaviours Based on a Hidden Markov Model.- Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |