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dc.contributor.authorAggarwal, Deeptien_US
dc.date.accessioned2015-09-18T20:09:55Z
dc.date.available2015-09-18T20:09:55Z
dc.date.issued2015-09-03en_US
dc.identifier.othervt_gsexam:6273en_US
dc.identifier.urihttp://hdl.handle.net/10919/56601
dc.description.abstractPlants have developed specific responses to external stimuli such as drought, cold, high salinity in soil, and precipitation in addition to internal developmental stimuli. These stimuli trigger signal transduction pathways in plants, leading to cellular adaptation. A signal transduction pathway is a network of entities that interact with one another in response to given stimulus. Such participating entities control and affect gene expression in response to stimulus . For computational purposes, a signal transduction pathway is represented as a network where nodes are biological molecules. The interaction of two nodes is a directed edge. A plethora of research has been conducted to understand signal transduction pathways. However, there are a limited number of approaches to explore and integrate signal transduction pathways. Therefore, we need a platform to integrate together and to expand the information of each signal transduction pathway. One of the major computational challenges in inferring signal transduction pathways is that the addition of new nodes and edges can affect the information flow between existing ones in an unknown manner. Here, I develop the Beacon inference engine to address these computational challenges. This software engine employs a network inference approach to predict new edges. First, it uses mutual information and context likelihood relatedness to predict edges from gene expression time-series data. Subsequently, it incorporates prior knowledge to limit false-positive predictions. Finally, a naive Bayes classifier is used to predict new edges. The Beacon inference engine predicts new edges with a recall rate 77.6% and precision 81.4%. 24% of the total predicted edges are new i.e., they are not present in the prior knowledge.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.subjectSignal Transduction Pathwaysen_US
dc.subjectGene Expressionen_US
dc.subjectInference Engineen_US
dc.titleInferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledgeen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairParikh, Devien_US
dc.contributor.committeechairHeath, Lenwood S.en_US
dc.contributor.committeememberYu, Guoqiangen_US
dc.contributor.committeememberGrene, Ruthen_US


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