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.

Bibliographic Details
Main Authors:	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
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-09-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-25893-w

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spelling	doaj-df8c1a08be1a4c64bc07660726b1c1e22021-09-26T11:45:04ZengNature Publishing GroupNature Communications2041-17232021-09-0112111510.1038/s41467-021-25893-wEvolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationshipsChia-Yi Cheng0Ying Li1Kranthi Varala2Jessica Bubert3Ji Huang4Grace J. Kim5Justin Halim6Jennifer Arp7Hung-Jui S. Shih8Grace Levinson9Seo Hyun Park10Ha Young Cho11Stephen P. Moose12Gloria M. Coruzzi13Department of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Horticulture and Landscape Architecture, Purdue UniversityDepartment of Horticulture and Landscape Architecture, Purdue UniversityDepartment of Crop Sciences, University of Illinois at Urbana-ChampaignDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Crop Sciences, University of Illinois at Urbana-ChampaignDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityDepartment of Crop Sciences, University of Illinois at Urbana-ChampaignDepartment of Biology, Center for Genomics and Systems Biology, New York UniversityPredicting 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.https://doi.org/10.1038/s41467-021-25893-w
collection	DOAJ
language	English
format	Article
sources	DOAJ
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
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-09-01
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.
url	https://doi.org/10.1038/s41467-021-25893-w
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

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