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In this thesis, parallel computing methods are used to construct a stochastic context-free grammar for analyzing the secondary structure of tRNA molecules. Stochastic context-free grammars are basically probabilistic languages that parse a sequence/molecule and output its probability of whether or not it belongs to the sequence family modeled by the grammar and at the same time predict the molecule's secondary structure. Stochastic context-free grammars can be converted from corresponding context-free grammars, by assigning probabilities to the grammar's production rules. The use of stochastic context-free grammars to analyze the secondary structure of RNA molecules was limited due to time and space complexity. In order to overcome such problems, we apply the parallel computing method to train a stochastic context-free grammar and use it to predict the secondary structures of tRNA molecules. The test results demonstrate that our parallel method is efficient and produces results that are at least better than current sequential methods. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S55. Source: Masters Abstracts International, Volume: 42-03, page: 0974. Adviser: Alioune Ngom. Thesis (M.Sc.)--University of Windsor (Canada), 2003.
Shi, Kaiyuan., "Parallel stochastic context-free grammar training for tRNA secondary structure prediction." (2003). Electronic Theses and Dissertations. 2991.