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
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/8453 |
| Date | 22 August 2012 |
| Creators | Zier-Vogel, Ryan |
| Contributors | Domaratzki, Michael (Computer Science), Durocher, Stephane (Computer Science) McKenna, Sean (Chemistry) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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