OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells
Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored t...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400321 |
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| author | Po‐Yuan Chen Tai‐Ming Ko |
| author_facet | Po‐Yuan Chen Tai‐Ming Ko |
| author_sort | Po‐Yuan Chen |
| collection | DOAJ |
| description | Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation. |
| format | Article |
| id | doaj-art-0ab01508ec2a43ea807a2ef3e11fcf3d |
| institution | DOAJ |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-0ab01508ec2a43ea807a2ef3e11fcf3d2025-08-20T02:55:48ZengWileyAdvanced Intelligent Systems2640-45672025-03-0173n/an/a10.1002/aisy.202400321OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory CellsPo‐Yuan Chen0Tai‐Ming Ko1Institute of Bioinformatics and Systems Biology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu 300 TaiwanInstitute of Bioinformatics and Systems Biology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu 300 TaiwanOxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation.https://doi.org/10.1002/aisy.202400321in silico cell annotationsinflammationsmachine learningsoxidative stressesreactive oxygen speciessingle‐cell transcriptional profiles |
| spellingShingle | Po‐Yuan Chen Tai‐Ming Ko OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells Advanced Intelligent Systems in silico cell annotations inflammations machine learnings oxidative stresses reactive oxygen species single‐cell transcriptional profiles |
| title | OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells |
| title_full | OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells |
| title_fullStr | OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells |
| title_full_unstemmed | OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells |
| title_short | OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells |
| title_sort | oxidative stress predictor a supervised learning approach for annotating cellular oxidative stress states in inflammatory cells |
| topic | in silico cell annotations inflammations machine learnings oxidative stresses reactive oxygen species single‐cell transcriptional profiles |
| url | https://doi.org/10.1002/aisy.202400321 |
| work_keys_str_mv | AT poyuanchen oxidativestresspredictorasupervisedlearningapproachforannotatingcellularoxidativestressstatesininflammatorycells AT taimingko oxidativestresspredictorasupervisedlearningapproachforannotatingcellularoxidativestressstatesininflammatorycells |