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dc.contributor.advisorTegner, Jesper
dc.contributor.authorWang, Liu-Wei
dc.date.accessioned2020-12-03T05:22:51Z
dc.date.available2020-12-03T05:22:51Z
dc.date.issued2020-11-11
dc.identifier.citationWang, L.-W. (2020). Prediction of Novel Virus–Host Protein Protein Interactions From Sequences and Infectious Disease Phenotypes. KAUST Research Repository. https://doi.org/10.25781/KAUST-ND1N5
dc.identifier.doi10.25781/KAUST-ND1N5
dc.identifier.urihttp://hdl.handle.net/10754/666246
dc.description.abstractInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, 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., signs and 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. We developed DeepViral, a deep learning based method that predicts protein– protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.
dc.language.isoen
dc.subjectHost pathogen interactions
dc.subjectinfectious diseases
dc.subjectvirus
dc.subjectprotein protein interactions
dc.titlePrediction of Novel Virus–Host Protein Protein Interactions From Sequences and Infectious Disease Phenotypes
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHoehndorf, Robert
dc.contributor.committeememberOmbao, Hernando
thesis.degree.disciplineStatistics
thesis.degree.nameMaster of Science
refterms.dateFOA2020-12-03T05:22:51Z
kaust.request.doiyes


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