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• W2522757360 abstract "New infectious viruses appear regularly, established ones fail to be eradicated, posing significant challenges to public health. Our lack of understanding of the intimate relationship between viruses and their hosts makes it difficult to develop effective therapies. Protein-protein interactions (PPIs) are key players in the cell generally and viruses exploit them for their purposes. Considerable progress has been made with HIV-1, the causative agent of AIDS, where experimental efforts have identified thousands of physical interactions and functional associations between the virus and the human host proteins. However, the complete and accurate repertoire of the physical interactome is still far from complete. Towards better defining the virus-host interactome, this dissertation complements experimental efforts by bridging different levels of biological information in a machine learning framework. Specifically, a wide array of genomic and proteomic data that could serve as direct and indirect feature evidence for virus, host PPIs was compiled. A supervised classification model was presented based on this data. A high quality label set was obtained by collecting experts' opinions on published interactions. A probabilistic framework was provided to estimate expert labeling accuracies and to obtain reliability scores for each interaction. Finally, to overcome data scarcity issues, we developed a multi-task learning strategy, where single tasks (learning the PPIs of each viral protein) shared parameters across different tasks based on their relatedness. The methods developed as part of this thesis can be easily extended to other host-virus systems as pertinent data become available. Numerous predictions of HIV-1, human interactions have subsequently been partially validated by experiments." @default.
• W2522757360 created "2016-09-30" @default.
• W2522757360 creator A5051657734 @default.
• W2522757360 creator A5065265042 @default.
• W2522757360 date "2011-01-01" @default.
• W2522757360 modified "2023-09-27" @default.
• W2522757360 title "Prediction of host, virus protein protein interactions" @default.
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