RT Journal Article
SR Electronic
T1 Prediction of novel virus–host interactions by integrating clinical symptoms and protein sequences
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.22.055095
DO 10.1101/2020.04.22.055095
A1 Wang Liu-Wei
A1 Şenay Kafkas
A1 Jun Chen
A1 Jesper Tegnér
A1 Robert Hoehndorf
YR 2020
UL http://biorxiv.org/content/early/2020/04/25/2020.04.22.055095.abstract
AB 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.