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dc.contributor.advisorKorkin, Dmitryeng
dc.contributor.advisorShyu, Chi-Reneng
dc.contributor.authorZhao, Naneng
dc.date.issued2013eng
dc.date.submitted2013 Springeng
dc.description.abstractCurrently, with the growth of experimental structural data on protein-protein interactions and larger protein complexes, the trend in computational biology and structural bioinformatics is towards applying such resources to model and analyze structures, functions, and evolutions of PPIs. Nevertheless, due to the rapid growth of the number of experimental structures, it becomes necessary to introduce bioinformatics methodologies, which rely on the advanced machine learning and information retrieval techniques, capable of handling complex and massive structural data. The research in this dissertation introduces and develops several computational methodologies to understand PPI 3D structures. First, we introduced an alignment-free similarity measure to detect structural similar PPI interfaces. This approach is capable of finding similar PPI interfaces formed by non-related protein subunits. Second, applying our similarity measure for PPIs, we showed our ability to use feature based interface similarity to classify and retrieve similar interface structures efficiently. Third, we used a set of simple protein interface structural features to test the classification and scoring performances for docked protein complexes, by using supervised and semi-supervised learning. Fourth, we analyzed the conservation patterns of charged residues located in PPI interfaces on a sampled set of PPI data. Last, we processed a genome-wide analysis of alternative splicing (AS) effects on human PPIs.eng
dc.format.extentxvi, 127 pageseng
dc.identifier.oclc872588594eng
dc.identifier.urihttps://hdl.handle.net/10355/37826
dc.identifier.urihttps://doi.org/10.32469/10355/37826eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2013 Freely available dissertations (MU)eng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.source.originalSubmitted by the University of Missouri--Columbia Graduate School.eng
dc.subjectprotein-protein interactionseng
dc.subjectmachine learningeng
dc.subjectinformation retrievaleng
dc.subjectdata analysiseng
dc.titleUnderstanding structure, function, and evolution of protein-protein interactions by computational modeling and analysiseng
dc.typeThesiseng
thesis.degree.disciplineHealth informatics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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