2026-09-21T06:11:09Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/668212022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Manolis Kellis.
Yen, Angela
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-11-01T19:48:54Z
2011-11-01T19:48:54Z
2011
2011
http://hdl.handle.net/1721.1/66821
757179627
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-102).
In this thesis, I leveraged computational methods on biological data to better understand gene regulation and development of the human body, as well as of the model organisms mouse and yeast. Firstly, I tackled biological questions with machine learning techniques by studying pre-transcriptional gene regulation through nucleosome positioning, which resulted in the identification of function-specific factors and improved predictive performance. Next, computational analysis enabled the discovery of genome-wide epigenetic modifications that play a foundational role in silencing for the monoallelic and monogenic expression of olfactory receptor genes in mice. Lastly, signatures of functional, bound RNA regions provide insight into a potential protocol-specific bias and produce a new avenue for de novo discovery of functional regions.
by Angela Yen.
M.Eng.
102 p.
eng
Massachusetts Institute of Technology
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
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Leveraging high-throughput datasets for studies of gene regulation
Thesis
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