MTIOT: Identifying HPV subtypes from multiple infection data.

ABSTRACT: 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

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC11755069 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature