Integrating Genomic Data and Interpretable Machine Learning Models to Identify Post-transcriptionally Controlled Response Regulators.

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dc.contributor.advisorCranos Williams, Chair
dc.contributor.advisorTerri Long, Graduate School Representative
dc.contributor.advisorEdgar Lobaton, Member
dc.contributor.advisorAlper Bozkurt, Member
dc.contributor.advisorCranos Williams, Minor
dc.contributor.advisorJose Alonso Bellver, Member
dc.contributor.authorSchmittling, Selene Roby
dc.date.accepted2024-11-19
dc.date.accessioned2024-11-22T13:30:41Z
dc.date.available2024-11-22T13:30:41Z
dc.date.defense2024-08-07
dc.date.issued2024-08-07
dc.date.released2024-11-22
dc.date.reviewed2024-08-29
dc.date.submitted2024-08-27
dc.degree.disciplineElectrical Engineering
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.identifier.otherdeg39305
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/44462
dc.titleIntegrating Genomic Data and Interpretable Machine Learning Models to Identify Post-transcriptionally Controlled Response Regulators.

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