dc.contributor.advisor | David Gifford. | en_US |
dc.contributor.author | Kang, Daniel D | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2016-12-22T16:29:41Z | |
dc.date.available | 2016-12-22T16:29:41Z | |
dc.date.copyright | 2015 | en_US |
dc.date.issued | 2015 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/106117 | |
dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. | en_US |
dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages [18]-[20]). | en_US |
dc.description.abstract | We introduce a new model called CCM+ that rectifies the inability of previous sequence-based methods to generalize across cell-types. We permit generalization by introducing arbitrary base-pair resolution covariates. We show that the addition of base-pair resolution chromatin accessibility covariate greatly aids in the prediction of cis-regulatory marks. Additionally, we show that by using cell-type specific covariates, CCM+ can generalize across cell-types. Finally, we show CCM+ can be used for downstream analysis that matches state-of-the-art methods when chromatin accessibility is used as a covariate. | en_US |
dc.description.statementofresponsibility | by Daniel Kang. | en_US |
dc.format.extent | 27 unnumbered pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about 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 | Exploring cis-regulatory models of the genome to predict epigenetic state and variation | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.oclc | 965798517 | en_US |