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Int. J. Mol. Sci. 2017, 18(6), 1135; doi:10.3390/ijms18061135

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

1
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore, Singapore
2
Institute of High Performance Computing, A*Star, 138632 Singapore, Singapore
3
National Supercomputing Centre, 138632 Singapore, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Susanna K. P. Lau
Received: 14 March 2017 / Revised: 18 May 2017 / Accepted: 19 May 2017 / Published: 25 May 2017
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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. View Full-Text
Keywords: influenza; zoonosis; machine learning influenza; zoonosis; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Eng, C.L.P.; Tong, J.C.; Tan, T.W. Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest. Int. J. Mol. Sci. 2017, 18, 1135.

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