Please use this identifier to cite or link to this item:
https://doi.org/10.3390/ijms18061135
DC Field | Value | |
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dc.title | Predicting zoonotic risk of influenza a viruses from host tropism protein signature using random forest | |
dc.contributor.author | Eng, C.L.P | |
dc.contributor.author | Tong, J.C | |
dc.contributor.author | Tan, T.W | |
dc.date.accessioned | 2020-09-14T08:03:55Z | |
dc.date.available | 2020-09-14T08:03:55Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Eng, C.L.P, Tong, J.C, Tan, T.W (2017). Predicting zoonotic risk of influenza a viruses from host tropism protein signature using random forest. International Journal of Molecular Sciences 18 (6) : A1135. ScholarBank@NUS Repository. https://doi.org/10.3390/ijms18061135 | |
dc.identifier.issn | 1661-6596 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/176089 | |
dc.description.abstract | Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. © 2017 by the authors. Licensee MDPI, Basel, Switzerland. | |
dc.source | Unpaywall 20200831 | |
dc.subject | amino acid sequence | |
dc.subject | area under the curve | |
dc.subject | Article | |
dc.subject | avian influenza | |
dc.subject | diagnostic test accuracy study | |
dc.subject | Influenza A virus | |
dc.subject | machine learning | |
dc.subject | nonhuman | |
dc.subject | physical chemistry | |
dc.subject | predictive value | |
dc.subject | random forest | |
dc.subject | receiver operating characteristic | |
dc.subject | retrospective study | |
dc.subject | sensitivity and specificity | |
dc.subject | viral tropism | |
dc.subject | zoonosis | |
dc.subject | animal | |
dc.subject | bird | |
dc.subject | epidemic | |
dc.subject | genetic database | |
dc.subject | genetics | |
dc.subject | host pathogen interaction | |
dc.subject | host range | |
dc.subject | human | |
dc.subject | influenza | |
dc.subject | Influenza A virus | |
dc.subject | machine learning | |
dc.subject | orthomyxovirus infection | |
dc.subject | reproducibility | |
dc.subject | theoretical model | |
dc.subject | viral tropism | |
dc.subject | virology | |
dc.subject | viral protein | |
dc.subject | Animals | |
dc.subject | Area Under Curve | |
dc.subject | Birds | |
dc.subject | Databases, Genetic | |
dc.subject | Disease Outbreaks | |
dc.subject | Host Specificity | |
dc.subject | Host-Pathogen Interactions | |
dc.subject | Humans | |
dc.subject | Influenza A virus | |
dc.subject | Influenza in Birds | |
dc.subject | Influenza, Human | |
dc.subject | Machine Learning | |
dc.subject | Models, Theoretical | |
dc.subject | Orthomyxoviridae Infections | |
dc.subject | Reproducibility of Results | |
dc.subject | Retrospective Studies | |
dc.subject | Viral Proteins | |
dc.subject | Viral Tropism | |
dc.subject | Zoonoses | |
dc.type | Article | |
dc.contributor.department | BIOCHEMISTRY | |
dc.description.doi | 10.3390/ijms18061135 | |
dc.description.sourcetitle | International Journal of Molecular Sciences | |
dc.description.volume | 18 | |
dc.description.issue | 6 | |
dc.description.page | A1135 | |
dc.published.state | Published | |
Appears in Collections: | Elements Staff Publications |
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10_3390_ijms18061135.pdf | 2.84 MB | Adobe PDF | OPEN | None | View/Download |
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