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Codon EvolutionMechanisms and Models$
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Gina M. Cannarozzi and Adrian Schneider

Print publication date: 2012

Print ISBN-13: 9780199601165

Published to Oxford Scholarship Online: May 2015

DOI: 10.1093/acprof:osobl/9780199601165.001.0001

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Distance measures and machine learning approaches for codon usage analyses

Distance measures and machine learning approaches for codon usage analyses

Chapter:
(p.229) Chapter 15 Distance measures and machine learning approaches for codon usage analyses
Source:
Codon Evolution
Author(s):

Fran Supek

Tomislav Šmuc

Publisher:
Oxford University Press
DOI:10.1093/acprof:osobl/9780199601165.003.0015

This chapter discusses various statistical approaches for codon usage analyses. It argues that introducing a supervised machine learning-based computational framework for codon usage analysis would lead to an increased sensitivity in detecting codon usage biases over commonly used unsupervised techniques, as well as better agreement to protein expression levels. It describes in detail how such a classifier-based approach was applied to prokaryotic genomes on a large scale, and what insights were gained from this analysis.

Keywords:   codon usage analysis, machine learning, classifier, supervised methods, prokaryotic genome

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