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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily i...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463701/ https://www.ncbi.nlm.nih.gov/pubmed/34561450 http://dx.doi.org/10.1038/s41467-021-25893-w |
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author | Cheng, Chia-Yi Li, Ying Varala, Kranthi Bubert, Jessica Huang, Ji Kim, Grace J. Halim, Justin Arp, Jennifer Shih, Hung-Jui S. Levinson, Grace Park, Seo Hyun Cho, Ha Young Moose, Stephen P. Coruzzi, Gloria M. |
author_facet | Cheng, Chia-Yi Li, Ying Varala, Kranthi Bubert, Jessica Huang, Ji Kim, Grace J. Halim, Justin Arp, Jennifer Shih, Hung-Jui S. Levinson, Grace Park, Seo Hyun Cho, Ha Young Moose, Stephen P. Coruzzi, Gloria M. |
author_sort | Cheng, Chia-Yi |
collection | PubMed |
description | Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine. |
format | Online Article Text |
id | pubmed-8463701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84637012021-10-22 Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships Cheng, Chia-Yi Li, Ying Varala, Kranthi Bubert, Jessica Huang, Ji Kim, Grace J. Halim, Justin Arp, Jennifer Shih, Hung-Jui S. Levinson, Grace Park, Seo Hyun Cho, Ha Young Moose, Stephen P. Coruzzi, Gloria M. Nat Commun Article Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine. Nature Publishing Group UK 2021-09-24 /pmc/articles/PMC8463701/ /pubmed/34561450 http://dx.doi.org/10.1038/s41467-021-25893-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cheng, Chia-Yi Li, Ying Varala, Kranthi Bubert, Jessica Huang, Ji Kim, Grace J. Halim, Justin Arp, Jennifer Shih, Hung-Jui S. Levinson, Grace Park, Seo Hyun Cho, Ha Young Moose, Stephen P. Coruzzi, Gloria M. Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463701/ https://www.ncbi.nlm.nih.gov/pubmed/34561450 http://dx.doi.org/10.1038/s41467-021-25893-w |
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