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dc.contributor.authorWagner, Mitchell Jamesen_US
dc.date.accessioned2018-09-19T08:00:43Z
dc.date.available2018-09-19T08:00:43Z
dc.date.issued2018-09-18en_US
dc.identifier.othervt_gsexam:17143en_US
dc.identifier.urihttp://hdl.handle.net/10919/85044
dc.description.abstractSignaling pathways are widely studied in systems biology. Several databases catalog our knowledge of these pathways, including the proteins and interactions that comprise them. However, high-quality curation of this information is slow and painstaking. As a result, many interactions still lack annotation concerning the pathways they participate in. A natural question that arises is whether or not it is possible to automatically leverage existing annotations to identify new interactions for inclusion in a given pathway. Here, we present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors (TFs) within a background interaction network. The key idea underlying RegLinker is the use of regular-language constraints to control the number of non-pathway edges present in the computed paths. We systematically evaluate RegLinker and alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker exhibits superior recovery of withheld pathway proteins and interactions. These results show the promise of our approach for prioritizing candidates for experimental study and the broader potential of automated analysis to attenuate difficulties of traditional manual inquiry.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.subjectRegular Languagesen_US
dc.subjectShortest Pathsen_US
dc.subjectSignaling Networksen_US
dc.titleReconstructing Signaling Pathways Using Regular-Language Constrained Pathsen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_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.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairMurali, T. M.en_US
dc.contributor.committeememberHeath, Lenwood S.en_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US


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