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Metagenomic Classification Using an Abstraction Augmented Markov Model.

Identifieur interne : 001365 ( PubMed/Curation ); précédent : 001364; suivant : 001366

Metagenomic 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.

RBID : pubmed:26618474

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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Metagenomic Classification Using an Abstraction Augmented Markov Model.

Metagenomic Classification Using an Abstraction Augmented Markov Model.

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Abstract

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