Abstract
Epstein-Barr virus (EBV) plays important roles in the origin and the progression of human carcinomas, e.g. diffuse large B cell tumors, T cell lymphomas, etc. Discovering EBV targeted human genes and signaling pathways is vital to understand EBV tumorigenesis. In this study we propose a noise-tolerant homolog knowledge transfer method to reconstruct functional protein-protein interactions (PPI) networks between Epstein-Barr virus and Homo sapiens. The training set is augmented via homolog instances and the homolog noise is counteracted by support vector machine (SVM). Additionally we propose two methods to define subcellular co-localization (i.e. stringent and relaxed), based on which to further derive physical PPI networks. Computational results show that the proposed method achieves sound performance of cross validation and independent test. In the space of 648,672 EBV-human protein pairs, we obtain 51,485 functional interactions (7.94%), 869 stringent physical PPIs and 46,050 relaxed physical PPIs. Fifty-eight evidences are found from the latest database and recent literature to validate the model. This study reveals that Epstein-Barr virus interferes with normal human cell life, such as cholesterol homeostasis, blood coagulation, EGFR binding, p53 binding, Notch signaling, Hedgehog signaling, etc. The proteome-wide predictions are provided in the supplementary file for further biomedical research.
Highlights
Virus-host interaction helps virus to hijack host cellular processes for survival and replication within its host
Epstein-Barr virus (EBV) is the first known human tumor virus that acts as the causative agent of infectious mononucleosis, and plays important roles in the origin or progression of B cell malignancies, e.g. Hodgkinlymphoma, diverse AIDS-associated lymphomas
The results of Multi-instance support vector machine (SVM) Novel indicate that the proposed model still works well when the Gene ontology (GO) knowledge of the gene/protein concerned is not available
Summary
Virus-host interaction helps virus to hijack host cellular processes for survival and replication within its host. In view of the small experimental EBV-human PPI networks, we propose a noise-tolerant homolog knowledge transfer method to explicitly augment the training data.
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