Predicting genomic interactions using deep learning
Name
1126663037-MIT.pdf
Size
3.51 MB
Format
Adobe PDF
Checksum (MD5)
15fa88ebf1127b7b9c6ddb434003f6cb
Author(s)
Syed, Tahin Fahmid.
Advisor(s)
David K. Gifford.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis: E.C.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 39-43).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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