Predicting genes associated with RNA methylation pathways using machine learning
Machine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans.
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-08-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-022-03821-y |
| _version_ | 1817999023092531200 |
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| author | Georgia Tsagkogeorga Helena Santos-Rosa Andrej Alendar Dan Leggate Oliver Rausch Tony Kouzarides Hendrik Weisser Namshik Han |
| author_facet | Georgia Tsagkogeorga Helena Santos-Rosa Andrej Alendar Dan Leggate Oliver Rausch Tony Kouzarides Hendrik Weisser Namshik Han |
| author_sort | Georgia Tsagkogeorga |
| collection | DOAJ |
| description | Machine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans. |
| first_indexed | 2024-04-14T03:02:20Z |
| format | Article |
| id | doaj.art-396efec7513649209949c08bf1dc3e5d |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-04-14T03:02:20Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-396efec7513649209949c08bf1dc3e5d2022-12-22T02:15:51ZengNature PortfolioCommunications Biology2399-36422022-08-015111010.1038/s42003-022-03821-yPredicting genes associated with RNA methylation pathways using machine learningGeorgia Tsagkogeorga0Helena Santos-Rosa1Andrej Alendar2Dan Leggate3Oliver Rausch4Tony Kouzarides5Hendrik Weisser6Namshik Han7STORM Therapeutics Ltd, Babraham Research CampusThe Gurdon Institute, University of CambridgeThe Gurdon Institute, University of CambridgeSTORM Therapeutics Ltd, Babraham Research CampusSTORM Therapeutics Ltd, Babraham Research CampusMilner Therapeutics Institute, University of CambridgeSTORM Therapeutics Ltd, Babraham Research CampusMilner Therapeutics Institute, University of CambridgeMachine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans.https://doi.org/10.1038/s42003-022-03821-y |
| spellingShingle | Georgia Tsagkogeorga Helena Santos-Rosa Andrej Alendar Dan Leggate Oliver Rausch Tony Kouzarides Hendrik Weisser Namshik Han Predicting genes associated with RNA methylation pathways using machine learning Communications Biology |
| title | Predicting genes associated with RNA methylation pathways using machine learning |
| title_full | Predicting genes associated with RNA methylation pathways using machine learning |
| title_fullStr | Predicting genes associated with RNA methylation pathways using machine learning |
| title_full_unstemmed | Predicting genes associated with RNA methylation pathways using machine learning |
| title_short | Predicting genes associated with RNA methylation pathways using machine learning |
| title_sort | predicting genes associated with rna methylation pathways using machine learning |
| url | https://doi.org/10.1038/s42003-022-03821-y |
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