University of Limerick Institutional Repository

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

DSpace Repository

Show simple item record

dc.contributor.author Tewari, Prerna
dc.contributor.author Kashdan, Eugene
dc.contributor.author Walsh, Cathal Dominic
dc.contributor.author Martin, Cara M.
dc.contributor.author Parnell, Andrew C.
dc.contributor.author O'Leary, John J.
dc.date.accessioned 2021-09-24T10:04:54Z
dc.date.available 2021-09-24T10:04:54Z
dc.date.issued 2021
dc.identifier.uri http://hdl.handle.net/10344/10608
dc.description peer-reviewed en_US
dc.description.abstract 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 programmes are expected to cause a reduction in the incidence rates of OPSCCs, these effects will not be evident in the foreseeable future. Secondary prevention strategies are currently not feasible due to an incomplete understanding of the natural history of oral HPV infections in OPSCCs. The key parameters that govern natural history models remain largely ill-defined for HPV related OPSCCs and cannot be easily inferred from experimental data. Mathematical models have been used to estimate some of these ill-defined parameters in cervical cancer, another HPV related cancer leading to successful implementation of cancer prevention strategies. We outline a “double-Bayesian” mathematical modelling approach, whereby, a Bayesian machine learning model first estimates the probability of an individual having an oral HPV infection, given OPSCC and other covariate information en_US
dc.language.iso eng en_US
dc.publisher Public Library of Science en_US
dc.relation.ispartofseries PLoS Computational Biology;17(8), e1009289
dc.subject OPSCC en_US
dc.subject Papilloma Virus en_US
dc.title Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse bayesian modelling en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1371/journal.pcbi.1009289
dc.contributor.sponsor HRB en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid ICE 2015-1037 en_US
dc.relation.projectid 17/CDA/4695 en_US
dc.relation.projectid 12/RC/2289 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ULIR


Browse

My Account

Statistics