Machine learning and hypothesis driven optimization of bull semen cryopreservation media
Abstract 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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25104-6 |
| _version_ | 1797977464704598016 |
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| author | Frankie Tu Maajid Bhat Patrick Blondin Patrick Vincent Mohsen Sharafi James D. Benson |
| author_facet | Frankie Tu Maajid Bhat Patrick Blondin Patrick Vincent Mohsen Sharafi James D. Benson |
| author_sort | Frankie Tu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T05:07:19Z |
| format | Article |
| id | doaj.art-4e5b61b0b295450ead837d6be36d8fc7 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T05:07:19Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4e5b61b0b295450ead837d6be36d8fc72022-12-25T12:12:50ZengNature PortfolioScientific Reports2045-23222022-12-0112111210.1038/s41598-022-25104-6Machine learning and hypothesis driven optimization of bull semen cryopreservation mediaFrankie Tu0Maajid Bhat1Patrick Blondin2Patrick Vincent3Mohsen Sharafi4James D. Benson5Department of Computer Science, Memorial University of NewfoundlandRo, Clinical StrategySemex AllianceSemex AllianceSemex AllianceDepartment of Biology, University of SaskatchewanAbstract 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.https://doi.org/10.1038/s41598-022-25104-6 |
| spellingShingle | Frankie Tu Maajid Bhat Patrick Blondin Patrick Vincent Mohsen Sharafi James D. Benson Machine learning and hypothesis driven optimization of bull semen cryopreservation media Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-25104-6 |
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