TY - JOUR T1 - Prediction of novel virus–host interactions by integrating clinical symptoms and protein sequences JF - bioRxiv DO - 10.1101/2020.04.22.055095 SP - 2020.04.22.055095 AU - Wang Liu-Wei AU - Şenay Kafkas AU - Jun Chen AU - Jesper Tegnér AU - Robert Hoehndorf Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/04/25/2020.04.22.055095.abstract N2 - Motivation Infectious diseases from novel viruses are becoming a major public health concern. Fast identification of virus–host interactions can reveal mechanistic insights of infectious diseases and shed light on potential treatments and drug discoveries. Current computational prediction methods for novel viruses are based only on protein sequences. Yet, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.Results We developed DeepViral, a deep learning method that predicts potential protein–protein interactions between human and viruses. First, human proteins and viruses were embedded in a shared space using their associated phenotypes, functions, taxonomic classification, as well as formalized background knowledge from biomedical ontologies. By extending a sequence learning model with phenotype features, our model can not only significantly improve over previous sequence-based approaches for inter-species interaction prediction, but also identify pathways of viral targets under a realistic experimental setup for novel viruses.Availability https://github.com/bio-ontology-research-group/DeepViralContact robert.hoehndorf{at}kaust.edu.saCompeting Interest StatementThe authors have declared no competing interest. ER -