ViRNN: A Deep Learning Model for Viral Host Prediction

Abstract Viral outbreaks are on the rise in the world, with the current outbreak of COVID-19 being among one of the worst thus far. Many of these outbreaks were the result of zoonotic transfer between species, and thus understanding and predicting the host of a virus is very important. With the rise of sequencing technologies it is becoming increasingly easy to sequence the full genomes of viruses, databases of publicly available viral genomes are widely available. We utilize a convolutional and recurrent neural network architecture (ViRNN) to predict the hosts for the Coronaviridae family (Coronaviruses) amongst the eleven most common hosts of this family. Our architecture performed with an overall accuracy of 90.55% on our test dataset, with a micro-average AUC-PR of 0.97. Performance was variable per host. ViRNN outperformed previously published methods like k-nearest neighbors and support vector machines, as well as previously published deep learning based methods. Saliency maps based on integrated gradients revealed a number of proteins in the viral genome that may be important interactions determining viral infection in hosts. Overall, this method provides an adaptable classifier capable of predicting host species from viral genomic sequence with high accuracy.