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Catching old influenza virus with a new Markov model
Ham Ching Lam
,
Daniel Boley
Research Computing
Computer Science and Engineering
Research output
:
Contribution to conference
›
Paper
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peer-review
Overview
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Dive into the research topics of 'Catching old influenza virus with a new Markov model'. Together they form a unique fingerprint.
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Keyphrases
Virus
100%
Influenza Virus
100%
Markov Model
100%
Genetic Distance
50%
Highly Similar
33%
Avian Influenza
16%
Mutation Rate
16%
Site-directed mutation
16%
Evolutionary Theory
16%
Markov Chain
16%
Surface Antigen
16%
Peptide Sequencing
16%
Protein Sequence
16%
Hemagglutinin Gene
16%
Consensus Sequence
16%
Distance Metric
16%
Virus Sequence
16%
Hamming Distance
16%
Model Estimates
16%
Poisson Process
16%
Hemagglutinin Protein
16%
RNA Virus
16%
Number of States
16%
Poisson Process Model
16%
Single State
16%
Alignment Tool
16%
Agricultural and Biological Sciences
Genetic Distance
100%
Hemagglutinin
66%
Stochastic Process
66%
Amino Acid Sequence
66%
Markov Chain
33%
Surface Antigens
33%
RNA Virus
33%
Avian Influenza
33%
Consensus Sequence
33%
Immunology and Microbiology
Virus
100%
Influenza Virus
100%
Genetic Distance
42%
Hemagglutinin
28%
Amino Acid Sequence
28%
RNA Virus
14%
Avian Influenza Virus
14%
Mutation Rate
14%
Membrane Antigen
14%
Process Model
14%
Consensus Sequence
14%
Medicine and Dentistry
Virus
100%
Influenza Virus
100%
Genetic Distance
42%
Peptide Sequence
28%
Hemagglutinin
28%
Base
14%
Membrane Antigen
14%
Mutation Rate
14%
Consensus Sequence
14%
Avian Influenza Virus
14%
RNA Virus
14%
Neuroscience
Markov Models
100%
Stochastic Process
50%
Amino Acid Sequence
50%
RNA Virus
25%
Consensus Sequence
25%
Membrane Antigen
25%