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dc.contributor.authorAltarawy, Doaa Abdelsalam Ahmed Mohameden_US
dc.date.accessioned2018-10-25T06:00:55Z
dc.date.available2018-10-25T06:00:55Z
dc.date.issued2017-05-02
dc.identifier.othervt_gsexam:11346en_US
dc.identifier.urihttp://hdl.handle.net/10919/85504
dc.description.abstractWith the abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior knowledge (PK), along with gene expression, greatly improves prediction accuracy, the accuracy remains low. PK in GRN inference can be categorized into noisy and curated. Several algorithms were proposed to incorporate noisy PK, but none address curated PK. Another challenge is that much of the PK is not stored in databases or not in a unified structured format to be accessible by inference algorithms. Moreover, no GRN inference method exists that supports post-prediction PK. This thesis addresses those limitations with three solutions: PEAK algorithm for integrating both curated and noisy PK, Online-PEAK for post-prediction interactive feedback, and DeTangle for visualization and navigation of GRNs. PEAK integrates both curated as well as noisy PK in GRN inference. We introduce a novel method for GRN inference, CurInf, to effectively integrate curated PK, and we use the previous method, Modified Elastic Net, for noisy PK, and we call it NoisInf. Using 100% curated PK, CurInf improves the AUPR accuracy score over NoisInf by 27.3% in synthetic data, 86.5% in E. coli data, and 31.1% in S. cerevisiae data. Moreover, we developed an online algorithm, online-PEAK, that enables the biologist to interact with the inference algorithm, PEAK, through a visual interface to add their domain experience about the structure of the GRN as a feedback to the system. We experimentally verified the ability of online-PEAK to achieve incremental accuracy when PK is added by the user, including true and false PK. Even when the noise in PK is 10 times more than true PK, online-PEAK performs better than inference without any PK. Finally, we present DeTangle, a Web server for interactive GRN prediction and visualization. DeTangle provides a seamless analysis of GRN starting from uploading gene expression, GRN inference, post-prediction feedback using online-PEAK, and visualization and navigation of the predicted GRN. More accurate prediction of GRN can facilitate studying complex molecular interactions, understanding diseases, and aiding drug design.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectGene regulationen_US
dc.subjectprior knowledgeen_US
dc.subjectgene regulatory network inferenceen_US
dc.subjectvisualizationen_US
dc.subjectmachine learningen_US
dc.titleDeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networksen_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.committeememberNorth, Christopher L.en_US
dc.contributor.committeememberGrene, Ruthen_US
dc.contributor.committeememberShaffer, Clifford A.en_US
dc.contributor.committeememberIsmail, Mahameden_US


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