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dc.contributor.advisorDavid K. Gifford.en_US
dc.contributor.authorSyed, Tahin Fahmid.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-12T17:41:26Z
dc.date.available2019-11-12T17:41:26Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122883
dc.descriptionThesis: E.C.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 39-43).en_US
dc.description.abstractPhysical promoter-enhancer and CTCF-CTCF interactions organize the human genome in 3-dimensions, and contribute to the regulation of gene expression. Hi-C and related approaches have enabled profiling of these interactions, though how the instructions for these interactions are encoded in the genome is still largely not understood. We develop a deep learning model, Deep3DGenome, to predict genomic interactions using both genomic sequence data and chromatin features. We find that a machine learning model that has anchor specific modules and uses rich chromatin features outperforms previous approaches at predicting 3D interactions.en_US
dc.description.statementofresponsibilityby Tahin Fahmid Syed.en_US
dc.format.extent43 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePredicting genomic interactions using deep learningen_US
dc.typeThesisen_US
dc.description.degreeE.C.S.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1126663037en_US
dc.description.collectionE.C.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-12T17:41:25Zen_US
mit.thesis.degreeen_US


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