Show simple item record

dc.contributor.supervisor Domaratzki, Michael (Computer Science) en_US
dc.contributor.author Zier-Vogel, Ryan
dc.date.accessioned 2012-08-22T20:17:24Z
dc.date.available 2012-08-22T20:17:24Z
dc.date.issued 2012-08-22
dc.identifier.uri http://hdl.handle.net/1993/8453
dc.description.abstract In this thesis I extend a class of grammars called conjunctive grammars to a stochastic form called stochastic conjunctive grammars. This extension allows the grammars to predict pseudoknotted RNA secondary structure. Since observing sec- ondary structure is hard and expensive to do with today's technology, there is a need for computational solutions to this problem. A conjunctive grammar can handle pseudoknotted structure because of the way one sequence is generated by combining multiple parse trees. I create several grammars that are designed to predict pseudoknotted RNA sec- ondary structure. One grammar is designed to predict all types of pseudoknots and the others are made to only predict a pseudoknot called H-type. These grammars are trained and tested and the results are collected. I am able to obtain a sensitivity of over 75% and a speci city of over 89% on H-type pseudoknots en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject pseudoknot en_US
dc.subject RNA en_US
dc.subject grammar en_US
dc.title Predicting RNA secondary structure using a stochastic conjunctive grammar en_US
dc.type info:eu-repo/semantics/masterThesis
dc.type master thesis en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Durocher, Stephane (Computer Science) McKenna, Sean (Chemistry) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2012 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics