Metagenomic Classification Using an Abstraction Augmented Markov Model.
Identifieur interne : 001365 ( PubMed/Curation ); précédent : 001364; suivant : 001366Metagenomic Classification Using an Abstraction Augmented Markov Model.
Auteurs : Xiujun Sylvia Zhu [États-Unis] ; Monnie Mcgee [États-Unis]Source :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2016.
Abstract
The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification.
DOI: 10.1089/cmb.2015.0141
PubMed: 26618474
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<front><div type="abstract" xml:lang="en">The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification.</div>
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