MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
Debra J. Knisley, Teresa W. Haynes
Robert A. Beeler
In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector.
The network correctly assigned a high probability of RNA-likeness to trees identified as RNA-like in the RAG database, and a low probability of RNA-likeness to those classified as not RNA-like in the RAG database. We then used the neural network to predict the RNA-likeness of all the trees of order 9. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel.
Thesis - unrestricted
Koessler, Denise Renee, "A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network." (2010). Electronic Theses and Dissertations. Paper 1684. https://dc.etsu.edu/etd/1684
Copyright by the authors.