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dc.contributor.authorGrageda, Andre
dc.date.accessioned2023-10-04T13:34:35Z
dc.date.available2023-10-04T13:34:35Z
dc.date.issued2023-09
dc.identifier.urihttp://hdl.handle.net/20.500.12648/13032
dc.description.abstractModeling and computational analysis can be used to crystallize, integrate, and extract knowledge from large datasets generated by biology, medicine, and next-generation sequencing. The use of probability models, multifactor hypothesis testing, and computational analyses is crucial to studies in systems biology. These studies provide insights into understanding large and diverse molecular biology data sets. It is no longer enough to study individual molecules, their properties, and their interactions with other molecules in cells and organisms. In addition to generating numerous case studies with unique data, such studies provide a limited understanding of the underlying complexity and dynamics of the leading mechanisms determining the states and behaviors of a whole biological system. Sequencing and multi-omics experiments generate big data needed to model processes, organization and behavior of biological systems in a more comprehensive, less biased manner. Analysis of such enormously heterogeneous and complex information requires mathematical models and computational algorithms. It is the motivation and challenge of current systems biology and medicine. Applied to cancer systems biology, we will consider basic probabilistic aspects of big data. We study skewed frequency distributions commonly observed in diverse omics experiments. We focus on modeling and developing computational algorithms to quantify big data's statistical characteristics, aiming for accurate and unbiased characterization of the systems variation. In several applications, we focus on the identification of the skewed distributions for quantification and differentiation of the of R-loop formation patterns in non-cancer, pre-malignant states and cancer genomes. Current studies involving R-loops rely on the S9.6 antibody which generates noisy signals. We show that using R-loop forming sequences for filtering specific S9.6 signals selects biologically meaningful signals. R-loops have been shown to play a role in tumorigenesis. Using our R-loop forming sequence enrichment method, we investigate the roles of R-loops in tumorigenesis across different detection modalities primarily in breast cancer.en_US
dc.language.isoen_USen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBirth-Death Process Modelen_US
dc.subjectR-loopsen_US
dc.subjectSkewed Distributionsen_US
dc.subjectR-loop Forming Sequenceen_US
dc.subjectBRCA1-mutationen_US
dc.subjectBreast Canceren_US
dc.titleSkewed Distribution Models: Data Analysis, Identification, and Applications in Biomolecular Systems and R-loop Biology of Canceren_US
dc.typeDissertationen_US
dc.description.versionNAen_US
refterms.dateFOA2023-10-04T13:34:36Z
dc.description.institutionUpstate Medical Universityen_US
dc.description.departmentBiochemistry & Molecular Biologyen_US
dc.description.degreelevelPhDen_US
dc.description.advisorKuznetsov, Vladimir
dc.date.semesterFall 2023en_US


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