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

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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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:EN
Published: Nature Portfolio 2021
Subjects:
Science
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Online Access:https://doaj.org/article/df8c1a08be1a4c64bc07660726b1c1e2
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https://doaj.org/article/df8c1a08be1a4c64bc07660726b1c1e2

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