Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance
Abstract Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhindered proliferation of the virus during...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98444-8 |
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| author | Brittany Knight Taylor Otwell Michael P. Coryell Jennifer Stone Phillip Davis Bryan Necciai Paul E. Carlson Shanmuga Sozhamannan Alyxandria M. Schubert Yi H. Yan |
| author_facet | Brittany Knight Taylor Otwell Michael P. Coryell Jennifer Stone Phillip Davis Bryan Necciai Paul E. Carlson Shanmuga Sozhamannan Alyxandria M. Schubert Yi H. Yan |
| author_sort | Brittany Knight |
| collection | DOAJ |
| description | Abstract Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhindered proliferation of the virus during the pandemic resulted in many variants with unique mutations. Some of these mutations could lead to signature erosion, where tests developed using the genetic sequence of an earlier version of the pathogen may produce false negative results when used to detect novel variants. In this study, we assessed the performance changes of 15 molecular assay designs when challenged with a variety of mutations that fall within the targeted region. Using data generated from this study, we trained and assessed the performance of seven different machine learning models to predict whether a specific set of mutations will result in significant change in the performance for a specific test design. The best performing model demonstrated acceptable performance with sensitivity of 82% and specificity of 87% when assessed using tenfold cross validation. Our findings highlighted the potential of using machine learning models to predict the impact of emerging mutations on the performance of specific molecular test designs. |
| format | Article |
| id | doaj-art-a4e938c6bb2b4c5ea60d6467e6c05011 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a4e938c6bb2b4c5ea60d6467e6c050112025-08-20T02:15:16ZengNature PortfolioScientific Reports2045-23222025-05-0115111110.1038/s41598-025-98444-8Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performanceBrittany Knight0Taylor Otwell1Michael P. Coryell2Jennifer Stone3Phillip Davis4Bryan Necciai5Paul E. Carlson6Shanmuga Sozhamannan7Alyxandria M. Schubert8Yi H. Yan9MRIGlobalMRIGlobalLaboratory of Mucosal Pathogens and Cellular Immunology, Division of Bacterial, Parasitic and Allergenic Products, Office of Vaccines Research and Review, Biologics Evaluation and Research, U.S. Food and Drug AdministrationMRIGlobalMRIGlobalDefense Biological Product Assurance Office (DBPAO), Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense (JPEO-CBRND), Joint Project Lead, Enabling Biotechnologies JPL-EBLaboratory of Mucosal Pathogens and Cellular Immunology, Division of Bacterial, Parasitic and Allergenic Products, Office of Vaccines Research and Review, Biologics Evaluation and Research, U.S. Food and Drug AdministrationDefense Biological Product Assurance Office (DBPAO), Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense (JPEO-CBRND), Joint Project Lead, Enabling Biotechnologies JPL-EBDivision of Microbiology, Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug AdministrationDivision of Microbiology, Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug AdministrationAbstract Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhindered proliferation of the virus during the pandemic resulted in many variants with unique mutations. Some of these mutations could lead to signature erosion, where tests developed using the genetic sequence of an earlier version of the pathogen may produce false negative results when used to detect novel variants. In this study, we assessed the performance changes of 15 molecular assay designs when challenged with a variety of mutations that fall within the targeted region. Using data generated from this study, we trained and assessed the performance of seven different machine learning models to predict whether a specific set of mutations will result in significant change in the performance for a specific test design. The best performing model demonstrated acceptable performance with sensitivity of 82% and specificity of 87% when assessed using tenfold cross validation. Our findings highlighted the potential of using machine learning models to predict the impact of emerging mutations on the performance of specific molecular test designs.https://doi.org/10.1038/s41598-025-98444-8Signature erosionqPCR performanceIn silico predictionFalse negative resultSupervised learning |
| spellingShingle | Brittany Knight Taylor Otwell Michael P. Coryell Jennifer Stone Phillip Davis Bryan Necciai Paul E. Carlson Shanmuga Sozhamannan Alyxandria M. Schubert Yi H. Yan Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance Scientific Reports Signature erosion qPCR performance In silico prediction False negative result Supervised learning |
| title | Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| title_full | Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| title_fullStr | Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| title_full_unstemmed | Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| title_short | Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| title_sort | using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance |
| topic | Signature erosion qPCR performance In silico prediction False negative result Supervised learning |
| url | https://doi.org/10.1038/s41598-025-98444-8 |
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