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Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compa...

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Autores principales: Gill, Mitchell, Anderson, Robyn, Hu, Haifei, Bennamoun, Mohammed, Petereit, Jakob, Valliyodan, Babu, Nguyen, Henry T., Batley, Jacqueline, Bayer, Philipp E., Edwards, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991976/
https://www.ncbi.nlm.nih.gov/pubmed/35395721
http://dx.doi.org/10.1186/s12870-022-03559-z
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author Gill, Mitchell
Anderson, Robyn
Hu, Haifei
Bennamoun, Mohammed
Petereit, Jakob
Valliyodan, Babu
Nguyen, Henry T.
Batley, Jacqueline
Bayer, Philipp E.
Edwards, David
author_facet Gill, Mitchell
Anderson, Robyn
Hu, Haifei
Bennamoun, Mohammed
Petereit, Jakob
Valliyodan, Babu
Nguyen, Henry T.
Batley, Jacqueline
Bayer, Philipp E.
Edwards, David
author_sort Gill, Mitchell
collection PubMed
description Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03559-z.
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spelling pubmed-89919762022-04-09 Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction Gill, Mitchell Anderson, Robyn Hu, Haifei Bennamoun, Mohammed Petereit, Jakob Valliyodan, Babu Nguyen, Henry T. Batley, Jacqueline Bayer, Philipp E. Edwards, David BMC Plant Biol Research Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03559-z. BioMed Central 2022-04-08 /pmc/articles/PMC8991976/ /pubmed/35395721 http://dx.doi.org/10.1186/s12870-022-03559-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gill, Mitchell
Anderson, Robyn
Hu, Haifei
Bennamoun, Mohammed
Petereit, Jakob
Valliyodan, Babu
Nguyen, Henry T.
Batley, Jacqueline
Bayer, Philipp E.
Edwards, David
Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title_full Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title_fullStr Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title_full_unstemmed Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title_short Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
title_sort machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991976/
https://www.ncbi.nlm.nih.gov/pubmed/35395721
http://dx.doi.org/10.1186/s12870-022-03559-z
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