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

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
Source :
Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f8c1a08be1a4c64bc07660726b1c1e2
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-021-25893-w