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dc.contributor.authorEid, Fatma Elzahraa Sobhyen_US
dc.date.accessioned2017-05-04T08:00:34Z
dc.date.available2017-05-04T08:00:34Z
dc.date.issued2017-05-03en_US
dc.identifier.othervt_gsexam:11072en_US
dc.identifier.urihttp://hdl.handle.net/10919/77581
dc.description.abstractThe world has proven unprepared for deadly viral outbreaks. Designing antiviral drugs and strategies requires a firm understanding of the interactions taken place between the proteins of the virus and human proteins. The current computational models for predicting these interactions consider only single viruses for which extensive prior knowledge is available. The two prediction frameworks in this dissertation, DeNovo and DeNovo-Human, make it possible for the first time to predict the interactions between any viral protein and human proteins. They further helped to answer critical questions about the Zika virus. DeNovo utilizes concepts from virology, bioinformatics, and machine learning to make predictions for novel viruses possible. It pools protein-protein interactions (PPIs) from different viruses sharing the same host. It further introduces taxonomic partitioning to make the reported performance reflect the situation of predicting for a novel virus. DeNovo avoids the expected low accuracy of such a prediction by introducing a negative sampling scheme that is based on sequence similarity. DeNovo achieved accuracy up to 81% and 86% when predicting for a new viral species and a new viral family, respectively. This result is comparable to the best achieved previously in single virus-host and intra-species PPI prediction cases. DeNovo predicts PPIs of a novel virus without requiring known PPIs for it, but with a limitation on the number of human proteins it can make predictions against. The second framework, DeNovo-Human, relaxes this limitation by forcing in-network prediction and random sampling while keeping the pooling technique of DeNovo. The accuracy and AUC are both promising ($>85%$, and $>91%$ respectively). DeNovo-Human facilitates predicting the virus-human PPI network. To demonstrate how the two frameworks can enrich our knowledge about virus behavior, I use them to answer interesting questions about the Zika virus. The research questions examine how the Zika virus enters human cells, fights the innate immune system, and causes microcephaly. The answers obtained are well supported by recently published Zika virus studies.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectProtein-Protein Interactionen_US
dc.subjectVirusen_US
dc.subjectMachine Learningen_US
dc.subjectZika Virusen_US
dc.titlePredicting the Interactions of Viral and Human Proteinsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairHeath, Lenwood S.en_US
dc.contributor.committeememberElHefnawi, Mahmoud M.en_US
dc.contributor.committeememberZhang, Liqingen_US
dc.contributor.committeememberOnufriev, Alexeyen_US
dc.contributor.committeememberHuang, Berten_US


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