Harnessing machine learning to guide phylogenetic-tree search algorithms

Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al. show how an artificial intelligence approach can reduce computational time without losing accuracy of tree inference.

Guardado en:

Detalles Bibliográficos
Autores principales:	Dana Azouri, Shiran Abadi, Yishay Mansour, Itay Mayrose, Tal Pupko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1bdcf3cd732e45cb83b0599ce656b10d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1bdcf3cd732e45cb83b0599ce656b10d
record_format	dspace
spelling	oai:doaj.org-article:1bdcf3cd732e45cb83b0599ce656b10d2021-12-02T14:23:30ZHarnessing machine learning to guide phylogenetic-tree search algorithms10.1038/s41467-021-22073-82041-1723https://doaj.org/article/1bdcf3cd732e45cb83b0599ce656b10d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22073-8https://doaj.org/toc/2041-1723Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al. show how an artificial intelligence approach can reduce computational time without losing accuracy of tree inference.Dana AzouriShiran AbadiYishay MansourItay MayroseTal PupkoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dana Azouri Shiran Abadi Yishay Mansour Itay Mayrose Tal Pupko Harnessing machine learning to guide phylogenetic-tree search algorithms
description	Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al. show how an artificial intelligence approach can reduce computational time without losing accuracy of tree inference.
format	article
author	Dana Azouri Shiran Abadi Yishay Mansour Itay Mayrose Tal Pupko
author_facet	Dana Azouri Shiran Abadi Yishay Mansour Itay Mayrose Tal Pupko
author_sort	Dana Azouri
title	Harnessing machine learning to guide phylogenetic-tree search algorithms
title_short	Harnessing machine learning to guide phylogenetic-tree search algorithms
title_full	Harnessing machine learning to guide phylogenetic-tree search algorithms
title_fullStr	Harnessing machine learning to guide phylogenetic-tree search algorithms
title_full_unstemmed	Harnessing machine learning to guide phylogenetic-tree search algorithms
title_sort	harnessing machine learning to guide phylogenetic-tree search algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1bdcf3cd732e45cb83b0599ce656b10d
work_keys_str_mv	AT danaazouri harnessingmachinelearningtoguidephylogenetictreesearchalgorithms AT shiranabadi harnessingmachinelearningtoguidephylogenetictreesearchalgorithms AT yishaymansour harnessingmachinelearningtoguidephylogenetictreesearchalgorithms AT itaymayrose harnessingmachinelearningtoguidephylogenetictreesearchalgorithms AT talpupko harnessingmachinelearningtoguidephylogenetictreesearchalgorithms
_version_	1718391413136162816

Harnessing machine learning to guide phylogenetic-tree search algorithms

Ejemplares similares