A predictive model for secondary RNA structure using graph theory and a neural network.

ABSTRACT:

Background

Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a v

SUBMITTER: Koessler DR 

PROVIDER: S-EPMC3026369 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature