MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
Debra J. Knisley
Teresa W. Haynes, Jeff R. Knisley
The secondary structures of ribonucleic acid (RNA) have been successfully modeled with graph-theoretic structures. Often, simple graphs are used to represent secondary RNA structures; however, in this research, a multigraph representation of RNA is used, in which vertices represent stems and edges represent the internal motifs. Any type of RNA secondary structure may be represented by a graph in this manner. We define novel graphical invariants to quantify the multigraphs and obtain characteristic descriptors of the secondary structures. These descriptors are used to train an artificial neural network (ANN) to recognize the characteristics of secondary RNA structure. Using the ANN, we classify the multigraphs as either RNA-like or not RNA-like. This classification method produced results similar to other classification methods. Given the expanding library of secondary RNA motifs, this method may provide a tool to help identify new structures and to guide the rational design of RNA molecules.
Thesis - Open Access
Rockney, Alissa Ann, "A Predictive Model Which Uses Descriptors of RNA Secondary Structures Derived from Graph Theory." (2011). Electronic Theses and Dissertations. Paper 1300. http://dc.etsu.edu/etd/1300
Copyright by the authors.