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Machine learning and hypothesis driven optimization of bull semen cryopreservation media
Cryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9790888/ https://www.ncbi.nlm.nih.gov/pubmed/36567337 http://dx.doi.org/10.1038/s41598-022-25104-6 |
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author | Tu, Frankie Bhat, Maajid Blondin, Patrick Vincent, Patrick Sharafi, Mohsen Benson, James D. |
author_facet | Tu, Frankie Bhat, Maajid Blondin, Patrick Vincent, Patrick Sharafi, Mohsen Benson, James D. |
author_sort | Tu, Frankie |
collection | PubMed |
description | Cryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the interactions between samples and the many components of extender media and is often compounded by industry irrelevant sample sizes. To address these challenges, here we demonstrate blank-slate optimization of bull sperm cryopreservation media by supervised machine learning. We considered two supervised learning models: artificial neural networks and Gaussian process regression (GPR). Eleven media components and initial concentrations were identified from publications in bull semen cryopreservation, and an initial 200 extender-post-thaw motility pairs were used to train and 32 extender-post-thaw motility pairs to test the machine learning algorithms. The median post-thaw motility after coupling differential evolution with GPR the increased from 52.6 ± 6.9% to 68.3 ± 6.0% at generations 7 and 17 respectively, with several media performing dramatically better than control media counterparts. This is the first study in which machine learning was used to determine the best combination of constituents to optimize bull sperm cryopreservation media, and provides a template for optimization in other cell types. |
format | Online Article Text |
id | pubmed-9790888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97908882022-12-27 Machine learning and hypothesis driven optimization of bull semen cryopreservation media Tu, Frankie Bhat, Maajid Blondin, Patrick Vincent, Patrick Sharafi, Mohsen Benson, James D. Sci Rep Article Cryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the interactions between samples and the many components of extender media and is often compounded by industry irrelevant sample sizes. To address these challenges, here we demonstrate blank-slate optimization of bull sperm cryopreservation media by supervised machine learning. We considered two supervised learning models: artificial neural networks and Gaussian process regression (GPR). Eleven media components and initial concentrations were identified from publications in bull semen cryopreservation, and an initial 200 extender-post-thaw motility pairs were used to train and 32 extender-post-thaw motility pairs to test the machine learning algorithms. The median post-thaw motility after coupling differential evolution with GPR the increased from 52.6 ± 6.9% to 68.3 ± 6.0% at generations 7 and 17 respectively, with several media performing dramatically better than control media counterparts. This is the first study in which machine learning was used to determine the best combination of constituents to optimize bull sperm cryopreservation media, and provides a template for optimization in other cell types. Nature Publishing Group UK 2022-12-25 /pmc/articles/PMC9790888/ /pubmed/36567337 http://dx.doi.org/10.1038/s41598-022-25104-6 Text en © The Author(s) 2022 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 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/) . |
spellingShingle | Article Tu, Frankie Bhat, Maajid Blondin, Patrick Vincent, Patrick Sharafi, Mohsen Benson, James D. Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title | Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title_full | Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title_fullStr | Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title_full_unstemmed | Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title_short | Machine learning and hypothesis driven optimization of bull semen cryopreservation media |
title_sort | machine learning and hypothesis driven optimization of bull semen cryopreservation media |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9790888/ https://www.ncbi.nlm.nih.gov/pubmed/36567337 http://dx.doi.org/10.1038/s41598-022-25104-6 |
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