AIMC Topic: Papillomavirus Infections
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Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.
Journal of clinical microbiology
Nov 22, 2019
This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore...
TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.
Scientific reports
Jul 31, 2017
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical n...
Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially ...
Mutagenic Potential ofBos taurus Papillomavirus Type 1 E6 Recombinant Protein: First Description.
BioMed research international
Dec 9, 2015
Bovine papillomavirus (BPV) is considered a useful model to study HPV oncogenic process. BPV interacts with the host chromatin, resulting in DNA damage, which is attributed to E5, E6, and E7 viral oncoproteins activity. However, the oncogenic mechani...
Deep Learning-Enhanced Hand-Driven Microfluidic Chip for Multiplexed Nucleic Acid Detection Based on RPA/CRISPR.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jun 1, 2025
The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However, existing PCR-based screening methods suffer from inadequate accessibility, which dampens the enthus...
[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].
Zhonghua zhong liu za zhi [Chinese journal of oncology]
Feb 23, 2025
Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from th...
A machine learning approach to predict HPV positivity of oropharyngeal squamous cell carcinoma.
Pathologica
Dec 1, 2024
HPV status is an important prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC), with HPV-positive tumors associated with better overall survival. To determine HPV status, we rely on the immunohistochemical investigation for expression ...
Deep-Learning-Based Evaluation of Dual Stain Cytology for Cervical Cancer Screening: A New Paradigm.
Journal of the National Cancer Institute
Nov 2, 2021
DeepHPV: a deep learning model to predict human papillomavirus integration sites.
Briefings in bioinformatics
Jul 20, 2021
Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integr...
Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles.
Carcinogenesis
Oct 16, 2019
From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage...
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