Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

Predicting complex phenotypes from genomic information is still a challenge. Here, the authors use an evolutionarily informed machine learning approach within and across species to predict genes affecting nitrogen utilization in crops, and show their approach is also useful in mammalian systems.

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Autores principales:	Chia-Yi Cheng, Ying Li, Kranthi Varala, Jessica Bubert, Ji Huang, Grace J. Kim, Justin Halim, Jennifer Arp, Hung-Jui S. Shih, Grace Levinson, Seo Hyun Park, Ha Young Cho, Stephen P. Moose, Gloria M. Coruzzi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/df8c1a08be1a4c64bc07660726b1c1e2
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spelling	oai:doaj.org-article:df8c1a08be1a4c64bc07660726b1c1e22021-12-02T17:26:56ZEvolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships10.1038/s41467-021-25893-w2041-1723https://doaj.org/article/df8c1a08be1a4c64bc07660726b1c1e22021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25893-whttps://doaj.org/toc/2041-1723Predicting complex phenotypes from genomic information is still a challenge. Here, the authors use an evolutionarily informed machine learning approach within and across species to predict genes affecting nitrogen utilization in crops, and show their approach is also useful in mammalian systems.Chia-Yi ChengYing LiKranthi VaralaJessica BubertJi HuangGrace J. KimJustin HalimJennifer ArpHung-Jui S. ShihGrace LevinsonSeo Hyun ParkHa Young ChoStephen P. MooseGloria M. CoruzziNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Chia-Yi Cheng Ying Li Kranthi Varala Jessica Bubert Ji Huang Grace J. Kim Justin Halim Jennifer Arp Hung-Jui S. Shih Grace Levinson Seo Hyun Park Ha Young Cho Stephen P. Moose Gloria M. Coruzzi Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
description	Predicting complex phenotypes from genomic information is still a challenge. Here, the authors use an evolutionarily informed machine learning approach within and across species to predict genes affecting nitrogen utilization in crops, and show their approach is also useful in mammalian systems.
format	article
author	Chia-Yi Cheng Ying Li Kranthi Varala Jessica Bubert Ji Huang Grace J. Kim Justin Halim Jennifer Arp Hung-Jui S. Shih Grace Levinson Seo Hyun Park Ha Young Cho Stephen P. Moose Gloria M. Coruzzi
author_facet	Chia-Yi Cheng Ying Li Kranthi Varala Jessica Bubert Ji Huang Grace J. Kim Justin Halim Jennifer Arp Hung-Jui S. Shih Grace Levinson Seo Hyun Park Ha Young Cho Stephen P. Moose Gloria M. Coruzzi
author_sort	Chia-Yi Cheng
title	Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
title_short	Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
title_full	Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
title_fullStr	Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
title_full_unstemmed	Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
title_sort	evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df8c1a08be1a4c64bc07660726b1c1e2
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

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