Please use this identifier to cite or link to this item: https://doi.org/10.3390/ijms18061135
Title: Predicting zoonotic risk of influenza a viruses from host tropism protein signature using random forest
Authors: Eng, C.L.P
Tong, J.C 
Tan, T.W 
Keywords: amino acid sequence
area under the curve
Article
avian influenza
diagnostic test accuracy study
Influenza A virus
machine learning
nonhuman
physical chemistry
predictive value
random forest
receiver operating characteristic
retrospective study
sensitivity and specificity
viral tropism
zoonosis
animal
bird
epidemic
genetic database
genetics
host pathogen interaction
host range
human
influenza
Influenza A virus
machine learning
orthomyxovirus infection
reproducibility
theoretical model
viral tropism
virology
viral protein
Animals
Area Under Curve
Birds
Databases, Genetic
Disease Outbreaks
Host Specificity
Host-Pathogen Interactions
Humans
Influenza A virus
Influenza in Birds
Influenza, Human
Machine Learning
Models, Theoretical
Orthomyxoviridae Infections
Reproducibility of Results
Retrospective Studies
Viral Proteins
Viral Tropism
Zoonoses
Issue Date: 2017
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
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.
Source Title: International Journal of Molecular Sciences
URI: https://scholarbank.nus.edu.sg/handle/10635/176089
ISSN: 1661-6596
DOI: 10.3390/ijms18061135
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