{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122883"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122883","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Predicting genomic interactions using deep learning","abstract":"Physical 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. 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We find that a machine learning model that has anchor specific modules and uses rich chromatin features outperforms previous approaches at predicting 3D interactions.","abstract_has_math":false,"creators":["Syed, Tahin Fahmid."],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["David K. Gifford."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:48Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["E.C.S."]},{"key":"dc:title","label":"Title","values":["Predicting genomic interactions using deep learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["David K. Gifford."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:contributor.other":["Massachusetts Institute of Technology. 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