Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences
Type
PreprintKAUST Department
Bio-Ontology Research Group (BORG)Biological and Environmental Sciences and Engineering (BESE) Division
Bioscience Program
Computational Bioscience Research Center (CBRC)
Computer Science
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-04-25Permanent link to this record
http://hdl.handle.net/10754/662660
Metadata
Show full item recordAbstract
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/DeepViralCitation
Liu-Wei, W., Kafkas, S., Chen, J., Tegner, J., & Hoehndorf, R. (2020). Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences. doi:10.1101/2020.04.22.055095Publisher
Cold Spring Harbor LaboratoryAdditional Links
http://biorxiv.org/lookup/doi/10.1101/2020.04.22.055095https://www.biorxiv.org/content/biorxiv/early/2020/04/25/2020.04.22.055095.full.pdf
Relations
Is Supplemented By:- [Software]
Title: bio-ontology-research-group/DeepViral: Source code for the DeepViral paper. Publication Date: 2020-04-22. github: bio-ontology-research-group/DeepViral Handle: 10754/668127
ae974a485f413a2113503eed53cd6c53
10.1101/2020.04.22.055095