Buch, Englisch, Band 1552, 221 Seiten, Paperback, Format (B × H): 178 mm x 254 mm, Gewicht: 445 g
Reihe: Methods in Molecular Biology
Methods and Protocols
Buch, Englisch, Band 1552, 221 Seiten, Paperback, Format (B × H): 178 mm x 254 mm, Gewicht: 445 g
Reihe: Methods in Molecular Biology
ISBN: 978-1-4939-8292-9
Verlag: Springer
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Zellbiologie
- Naturwissenschaften Biowissenschaften Molekularbiologie
- Naturwissenschaften Biowissenschaften Biowissenschaften DNA und Transgene Organismen
- Naturwissenschaften Biowissenschaften Proteinforschung
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
Weitere Infos & Material
Introduction 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.
