dc.contributor.advisor | David K. Gifford. | en_US |
dc.contributor.author | Syed, Tahin Fahmid. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2019-11-12T17:41:26Z | |
dc.date.available | 2019-11-12T17:41:26Z | |
dc.date.copyright | 2019 | en_US |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/122883 | |
dc.description | Thesis: E.C.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 39-43). | en_US |
dc.description.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. 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.statementofresponsibility | by Tahin Fahmid Syed. | en_US |
dc.format.extent | 43 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT 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.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Predicting genomic interactions using deep learning | en_US |
dc.type | Thesis | en_US |
dc.description.degree | E.C.S. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.oclc | 1126663037 | en_US |
dc.description.collection | E.C.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
dspace.imported | 2019-11-12T17:41:25Z | en_US |
mit.thesis.degree | | en_US |