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dc.contributor.advisorLiu, Jun
dc.contributor.authorhu, zhirui
dc.date.accessioned2019-12-11T09:41:13Z
dash.embargo.terms2020-05-01
dc.date.created2019-11
dc.date.issued2019-09-10
dc.date.submitted2019
dc.identifier.citationhu, zhirui. 2019. Information Integration via Bayesian and Neural Network Models With Applications to Biology. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42013062*
dc.description.abstractThis thesis is comprised of three parts: 1) we proposed a new method to model convergent rate changes of genomic elements on phylogenetic trees and detect the association between the rate shifts and the convergent phenotypes; 2) we introduced a novel approach for nonparmateric regression using neural networks when the variables have measurement errors; 3) we developed a new method for imputation and clustering for single cell RNA sequencing data.
dc.description.sponsorshipStatistics
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectBayesian statistics, Phylogenetics, deep learning, computational biology
dc.titleInformation Integration via Bayesian and Neural Network Models With Applications to Biology
dc.typeThesis or Dissertation
dash.depositing.authorhu, zhirui
dash.embargo.until2020-05-01
dc.date.available2019-12-11T09:41:13Z
thesis.degree.date2019
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberEdwards, Scott
dc.contributor.committeeMemberLin, Xihong
dc.type.materialtext
thesis.degree.departmentStatistics
thesis.degree.departmentStatistics
dash.identifier.vireo
dash.author.emailxyz111131@gmail.com


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