Graphical Invariants and Molecular Descriptors for Secondary RNA Structures

D.J. Knisley and J.R. Knisley (USA)


Secondary RNA structure; graph theory; neural network; QSAR


There are many parameters that can be used to test biologi cal relevance of graph theoretic structures. In this paper, we characterize secondary RNA structures that are modeled as by graph theoretic trees. We use variations on the domi nation number of a graph, as well as the diameter, radius, and blocks of associated line graphs of each tree to define several graphical invariants. We also use five well known molecular descriptors from classical chemical graph theory to quantify each tree. A neural network allows us to explore if graph theoretic invariants can be used in conjunction with classical molecular descriptors in studying secondary RNA structures.

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