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    Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences

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    Preprintfile1.pdf
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    Type
    Preprint
    Authors
    Wang, Liu-Wei cc
    Kafkas, Senay cc
    Chen, Jun cc
    Tegner, Jesper cc
    Hoehndorf, Robert cc
    KAUST 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
    2020-04-25
    Permanent link to this record
    http://hdl.handle.net/10754/662660
    
    Metadata
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    Abstract
    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/DeepViral
    Citation
    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:10.1101/2020.04.22.055095
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.04.22.055095
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.04.22.055095
    https://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
    Scopus Count
    Collections
    Bio-Ontology Research Group (BORG); Biological and Environmental Science and Engineering (BESE) Division; Preprints; Bioscience Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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