AIMC Topic: Alphapapillomavirus

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Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

PLoS computational biology Aug 20, 2021
The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination progr...
Adult Male Middle Aged Machine Learning Female Aged Probability Oropharyngeal Neoplasms Alphapapillomavirus Squamous Cell Carcinoma of Head and Neck Bayes Theorem SEER Program Humans
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Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 22, 2021
Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer. Automatic detection of HPV in digitized pathology tissues using in situ hybridisation (ISH) is a difficult task due to the variability and complexity of s...
Humans Papillomaviridae Oropharyngeal Neoplasms Alphapapillomavirus Deep Learning Carcinoma Attention
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Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

The Laryngoscope Jul 13, 2020
OBJECTIVES: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status.
Oropharyngeal Neoplasms Alphapapillomavirus Magnetic Resonance Imaging Predictive Value of Tests Feasibility Studies Male Middle Aged ROC Curve Papillomavirus Infections Logistic Models Squamous Cell Carcinoma of Head and Neck Humans Retrospective Studies Machine Learning Female Aged
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Machine Learning Prediction of Extracapsular Extension in Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Jun 30, 2020
OBJECTIVE: To determine whether machine learning (ML) can predict the presence of extracapsular extension (ECE) prior to treatment, using common oncologic variables, in patients with human papillomavirus (HPV)-associated oropharyngeal squamous cell c...
Oropharyngeal Neoplasms Alphapapillomavirus Extranodal Extension Logistic Models Squamous Cell Carcinoma of Head and Neck Databases, Factual Middle Aged Prognosis ROC Curve Retrospective Studies Female Papillomavirus Infections Humans Aged Machine Learning Male
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Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.

European radiology Jun 26, 2020
OBJECTIVE: To compare the CT texture feature reproducibility of 2D and 3D segmentations and their machine learning (ML)-based classifications for predicting human papilloma virus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).
Humans Machine Learning Algorithms Retrospective Studies Tomography, X-Ray Computed Female Head and Neck Neoplasms Regression Analysis Diagnosis, Differential Image Processing, Computer-Assisted Area Under Curve Reproducibility of Results Papillomavirus Infections Observer Variation Male Squamous Cell Carcinoma of Head and Neck Alphapapillomavirus Imaging, Three-Dimensional
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An improved method for predicting interactions between virus and human proteins.

Journal of bioinformatics and computational biology Jul 10, 2016
The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein-protein interactions (PPIs), but most of the computational methods ...
Alphapapillomavirus Amino Acids Hepacivirus Host-Pathogen Interactions Gene Ontology Computational Biology Humans Viral Proteins Support Vector Machine Reproducibility of Results Protein Interaction Mapping Databases, Protein
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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...
Models, Genetic Neoplasm Proteins Viral Proteins Uterine Cervical Neoplasms Alphapapillomavirus Deep Learning Humans Virus Integration Papillomavirus Infections Female
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