A predictive model for secondary RNA structure using graph theory and a neural network
by Koessler, Denise, M.S., EAST TENNESSEE STATE UNIVERSITY, 2010, 55 pages; 1484815

Abstract:

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.

 
AdvisersTeresa Haynes; Debra Knisley
SchoolEAST TENNESSEE STATE UNIVERSITY
SourceMAI/ 48-05, p. , Jun 2010
Source TypeThesis
SubjectsMolecular biology; Applied mathematics; Mathematics; Bioinformatics
Publication Number1484815
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