ArticleFrontiers in artificial intelligence2022
A Straightforward HPV16 Lineage Classification Based on Machine Learning.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- A Novel Method for Predicting Oncogenic Types of Human Papillomavirus.Diagnostics (Basel, Switzerland) · 2025Article
- Genetic variation of E6 and E7 genes of human papillomavirus type 16 from central China.Virology journal · 2023Article
- A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples.Frontiers in microbiology · 2023Review
- Epidemiology and Molecular Biology of HPV Variants in Cervical Cancer: The State of the Art in Mexico.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human Papillomavirus (HPV) is the causal agent of 5% of cancers worldwide and the main cause of cervical cancer and it is also associated with a significant percentage of oropharyngeal and anogenital cancers. More than 60% of cervical cancers are caused by HPV16 genotype, which has been classified into lineages (A, B, C, and D). Lineages are related to the progression of cervical cancer and the current method to assess lineages is by building a Maximum Likelihood Tree (MLT); which is slow, it cannot assess poor sequenced samples, and annotation is done manually. In this study, we have developed a new model to assess HPV16 lineage using machine learning tools. A total of 645 HPV16 genomes were analyzed using Genome-Wide Association Study (GWAS), which identified 56 lineage-specific Single Nucleotide Polymorphisms (SNPs). From the SNPs found, training-test models were constructed using different algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-nearest neighbor (KNN). A distinct set of HPV16 sequences (
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Registered trials
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