Global ETD Search

1	A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network. Koessler, Denise Renee 08 May 2010 (has links) (PDF) 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. graph theory artificial neural network graph models math modeling secondary RNA RNA structure RNA as Graphs RNA tree graph application of graph theory vertex identification merge predictive model Discrete Mathematics and Combinatorics Mathematics Physical Sciences and Mathematics