Chaos game representation and its applications in bioinformatics

Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency ma...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hannah Franziska Löchel, Dominik Heider
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Chaos game representation Bioinformatics Sequence analysis Alignment-free sequence comparison DNA and protein encoding Machine learning Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/fb3dc2c1d611491eb0e36dcbadfcea88
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fb3dc2c1d611491eb0e36dcbadfcea88
record_format	dspace
spelling	oai:doaj.org-article:fb3dc2c1d611491eb0e36dcbadfcea882021-11-30T04:15:25ZChaos game representation and its applications in bioinformatics2001-037010.1016/j.csbj.2021.11.008https://doaj.org/article/fb3dc2c1d611491eb0e36dcbadfcea882021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004736https://doaj.org/toc/2001-0370Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.Hannah Franziska LöchelDominik HeiderElsevierarticleChaos game representationBioinformaticsSequence analysisAlignment-free sequence comparisonDNA and protein encodingMachine learningBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6263-6271 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chaos game representation Bioinformatics Sequence analysis Alignment-free sequence comparison DNA and protein encoding Machine learning Biotechnology TP248.13-248.65
spellingShingle	Chaos game representation Bioinformatics Sequence analysis Alignment-free sequence comparison DNA and protein encoding Machine learning Biotechnology TP248.13-248.65 Hannah Franziska Löchel Dominik Heider Chaos game representation and its applications in bioinformatics
description	Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.
format	article
author	Hannah Franziska Löchel Dominik Heider
author_facet	Hannah Franziska Löchel Dominik Heider
author_sort	Hannah Franziska Löchel
title	Chaos game representation and its applications in bioinformatics
title_short	Chaos game representation and its applications in bioinformatics
title_full	Chaos game representation and its applications in bioinformatics
title_fullStr	Chaos game representation and its applications in bioinformatics
title_full_unstemmed	Chaos game representation and its applications in bioinformatics
title_sort	chaos game representation and its applications in bioinformatics
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fb3dc2c1d611491eb0e36dcbadfcea88
work_keys_str_mv	AT hannahfranziskalochel chaosgamerepresentationanditsapplicationsinbioinformatics AT dominikheider chaosgamerepresentationanditsapplicationsinbioinformatics
_version_	1718406841209192448

Chaos game representation and its applications in bioinformatics

Ejemplares similares