MTIOT: Identifying HPV subtypes from multiple infection data
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehens...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004288 |
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| author | Qi Zhao Tianjun Zhou Lin Li Guofan Hong Luonan Chen |
| author_facet | Qi Zhao Tianjun Zhou Lin Li Guofan Hong Luonan Chen |
| author_sort | Qi Zhao |
| collection | DOAJ |
| description | Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice. |
| format | Article |
| id | doaj-art-3395ada1dc3848b29c38158098915252 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-3395ada1dc3848b29c381580989152522025-01-04T04:56:14ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127149159MTIOT: Identifying HPV subtypes from multiple infection dataQi Zhao0Tianjun Zhou1Lin Li2Guofan Hong3Luonan Chen4Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Molecular Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, ChinaKey Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, ChinaState Key Laboratory of Molecular Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China; Corresponding author.Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; Corresponding author at: Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice.http://www.sciencedirect.com/science/article/pii/S2001037024004288HPVMachine LearningCervical carcinomaConvolution |
| spellingShingle | Qi Zhao Tianjun Zhou Lin Li Guofan Hong Luonan Chen MTIOT: Identifying HPV subtypes from multiple infection data Computational and Structural Biotechnology Journal HPV Machine Learning Cervical carcinoma Convolution |
| title | MTIOT: Identifying HPV subtypes from multiple infection data |
| title_full | MTIOT: Identifying HPV subtypes from multiple infection data |
| title_fullStr | MTIOT: Identifying HPV subtypes from multiple infection data |
| title_full_unstemmed | MTIOT: Identifying HPV subtypes from multiple infection data |
| title_short | MTIOT: Identifying HPV subtypes from multiple infection data |
| title_sort | mtiot identifying hpv subtypes from multiple infection data |
| topic | HPV Machine Learning Cervical carcinoma Convolution |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004288 |
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