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dc.contributor.advisorParkes, David C.
dc.contributor.authorZou, James Yang
dc.date.accessioned2014-02-25T21:54:12Z
dc.date.issued2014-02-25
dc.date.submitted2014
dc.identifier.citationZou, James Yang. 2014. Algorithms and Models for Genome Biology. Doctoral dissertation, Harvard University.en_US
dc.identifier.otherhttp://dissertations.umi.com/gsas.harvard:11280en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11745714
dc.description.abstractNew advances in genomic technology make it possible to address some of the most fundamental questions in biology for the first time. They also highlight a need for new approaches to analyze and model massive amounts of complex data. In this thesis, I present six research projects that illustrate the exciting interaction between high-throughput genomic experiments, new machine learning algorithms, and mathematical modeling. This interdisci- plinary approach gives insights into questions ranging from how variations in the epigenome lead to diseases across human populations to how the slime mold finds the shortest path. The algorithms and models developed here are also of interest to the broader machine learning community, and have applications in other domains such as text modeling.en_US
dc.description.sponsorshipMathematicsen_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.subjectComputer scienceen_US
dc.subjectBioinformaticsen_US
dc.subjectalgorithmsen_US
dc.subjectepigenomicsen_US
dc.subjectmachine learningen_US
dc.titleAlgorithms and Models for Genome Biologyen_US
dc.typeThesis or Dissertationen_US
dash.depositing.authorZou, James Yang
dc.date.available2014-02-25T21:54:12Z
thesis.degree.date2014en_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorHarvard Universityen_US
thesis.degree.leveldoctoralen_US
thesis.degree.namePh.D.en_US
dc.contributor.committeeMemberBrenner, Michaelen_US
dc.contributor.committeeMemberBernstein, Bradleyen_US
dash.contributor.affiliatedZou, James


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