2026-09-18T19:49:12Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/792402022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David K. Gifford.
Syed, Tahin Fahmid
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-06-17T19:49:54Z
2013-06-17T19:49:54Z
2013
2013
http://hdl.handle.net/1721.1/79240
845324064
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. [37]-40).
Recent advancements in sequencing technology have made it possible to study the mechanisms of gene regulation, such as protein-DNA binding, at greater resolution and on a greater scale than was previously possible. We present an expectation-maximization learning algorithm that identifies enriched spatial relationships between motifs in sets of DNA sequences. For example, the method will identify spatially constrained motifs colocated in the same regulatory region. We apply our method to biological sequence data and recover previously known prokaryotic promoter spacing constraints demonstrating that joint learning of motifs and spacing constraints is superior to other methods for this task.
by Tahin Fahmid Syed.
S.M.
40 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Methods for identifying regulatory grammars
Thesis
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