Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice
To enrich micronutrients in rice through breeding and to identify biofortified donor lines, screening a large diversity panel of major subspecies of rice accessions for their ionomes is necessary.Inductively coupled plasma optical emission spectroscopy was deployed to profile grain ionomes from 1100...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/132704 |
| _version_ | 1855534023294582784 |
|---|---|
| author | Tiozon, Rhowell N. Buenafe, Reuben James Jain, Vinay Sen, Partha Molina, Lilia R. Anacleto, Roslen Sreenivasulu, Nese |
| author_browse | Anacleto, Roslen Buenafe, Reuben James Jain, Vinay Molina, Lilia R. Sen, Partha Sreenivasulu, Nese Tiozon, Rhowell N. |
| author_facet | Tiozon, Rhowell N. Buenafe, Reuben James Jain, Vinay Sen, Partha Molina, Lilia R. Anacleto, Roslen Sreenivasulu, Nese |
| author_sort | Tiozon, Rhowell N. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | To enrich micronutrients in rice through breeding and to identify biofortified donor lines, screening a large diversity panel of major subspecies of rice accessions for their ionomes is necessary.Inductively coupled plasma optical emission spectroscopy was deployed to profile grain ionomes from 1100 rice accessions. Classification models derived for multidimensional ionomics data using a random forest model, support vector machine, and artificial neural network predicted three distinct groups that differ in ionomic levels with higher prediction accuracy (76.8%–85.1%). While class A showed inferior lines in mineral content, class B and C represent superior lines with enriched multiple minerals. The identified contrasting lines from the modeling classifications were milled and profiled for minerals and toxic elements using inductively coupled plasma mass spectrometry and revealed donors with an elevated mineral content even higher than the Zn‐biofortified rice lines.Overall, the result of this study provided new insights into ionomic variation among subspecies with a preferential enrichment of minerals in temperate japonica and admixed populations coincided with increased grain protein content, while in indica median mineral content is low while selected for increased amylose content.Artificial intelligence models were developed to predict grains enriched with multiple minerals. |
| format | Journal Article |
| id | CGSpace132704 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1327042025-12-08T10:11:39Z Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice Tiozon, Rhowell N. Buenafe, Reuben James Jain, Vinay Sen, Partha Molina, Lilia R. Anacleto, Roslen Sreenivasulu, Nese grain cells machine learning micronutrients rice To enrich micronutrients in rice through breeding and to identify biofortified donor lines, screening a large diversity panel of major subspecies of rice accessions for their ionomes is necessary.Inductively coupled plasma optical emission spectroscopy was deployed to profile grain ionomes from 1100 rice accessions. Classification models derived for multidimensional ionomics data using a random forest model, support vector machine, and artificial neural network predicted three distinct groups that differ in ionomic levels with higher prediction accuracy (76.8%–85.1%). While class A showed inferior lines in mineral content, class B and C represent superior lines with enriched multiple minerals. The identified contrasting lines from the modeling classifications were milled and profiled for minerals and toxic elements using inductively coupled plasma mass spectrometry and revealed donors with an elevated mineral content even higher than the Zn‐biofortified rice lines.Overall, the result of this study provided new insights into ionomic variation among subspecies with a preferential enrichment of minerals in temperate japonica and admixed populations coincided with increased grain protein content, while in indica median mineral content is low while selected for increased amylose content.Artificial intelligence models were developed to predict grains enriched with multiple minerals. 2024-03 2023-11-03T12:32:05Z 2023-11-03T12:32:05Z Journal Article https://hdl.handle.net/10568/132704 en Open Access application/pdf Wiley Tiozon, Rhowell N., Reuben JQ Buenafe, Vinay Jain, Partha Sen, Lilia R. Molina, Roslen Anacleto, and Nese Sreenivasulu. "Machine learning technique unraveled subspecies‐specific ionomic variation with the preferential mineral enrichment in rice." Cereal Chemistry (2023). |
| spellingShingle | grain cells machine learning micronutrients rice Tiozon, Rhowell N. Buenafe, Reuben James Jain, Vinay Sen, Partha Molina, Lilia R. Anacleto, Roslen Sreenivasulu, Nese Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title | Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title_full | Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title_fullStr | Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title_full_unstemmed | Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title_short | Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice |
| title_sort | machine learning technique unraveled subspecies specific ionomic variation with the preferential mineral enrichment in rice |
| topic | grain cells machine learning micronutrients rice |
| url | https://hdl.handle.net/10568/132704 |
| work_keys_str_mv | AT tiozonrhowelln machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT buenafereubenjames machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT jainvinay machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT senpartha machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT molinaliliar machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT anacletoroslen machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice AT sreenivasulunese machinelearningtechniqueunraveledsubspeciesspecificionomicvariationwiththepreferentialmineralenrichmentinrice |