Computational Biology of Gene Transcriptional Regulation

dc.contributor.advisorZhang, Michael Q.
dc.creatorXie, Peng
dc.date.accessioned2020-07-29T15:03:31Z
dc.date.available2020-07-29T15:03:31Z
dc.date.created2018-05
dc.date.issued2018-05
dc.date.submittedMay 2018
dc.date.updated2020-07-29T15:03:32Z
dc.description.abstractGene expression is the bridge between genetic information to biological function. Development of biotechnologies has enabled comprehensive profiling of gene expression, especially at the level of transcriptional regulation. The huge scale of data generated has reformed the way of biological studies and computational biology, or bioinformatics, analysis has become crucial. In this dissertation, I summarize three discoveries enabled by computational biology approaches. In the first project, I collaborated with experimental biologists and reported a previously unknown connection between enhancer transcription and gene regulation. In the second project, I proposed a novel analysis methodology that identified enhancer transcripts as targets to modulate gene expression. In the third project, I applied machine learning techniques that efficiently identified key cell types from heterogeneous samples by transcriptome profiling.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/8747
dc.language.isoen
dc.rights©2018 Peng Xie. All rights reserved.
dc.subjectGenetic transcription—Regulation
dc.subjectMachine learning
dc.subjectComputational biology
dc.subjectBiotechnology—Research
dc.subjectBioinformatics
dc.titleComputational Biology of Gene Transcriptional Regulation
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentBiology - Molecular and Cell Biology
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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