Name
Title | ||
|---|---|---|
Impact of an enhanced thermodynamic model on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction |
Abstract | ||
|---|---|---|
RNA has important structural, functional, and regulatory parts in the cell as well as a critical role in multiple stages of protein synthesis. An RNA molecule's shape largely determines its function in an organic system. Accordingly, computational RNA structural prediction methods are of significant interest. For ab initio cases where only an RNA sequence is known, structure prediction techniques typically employ free energy minimization of a given RNA molecule via a thermodynamic model. Unfortunately, the minimum free energy structure is rarely the native structure. This is thought to be due to errors in the experimentally determined thermodynamic model parameters. RnaPredict is an evolutionary algorithm designed for the prediction of RNA secondary structure; it currently utilizes the stacking-energy thermodynamic models INN and INN-HB. The effect of an enhanced model, efn2, on RnaPredict is investigated. The efn2 model significantly improved the sensitivity and specificity of the majority of structures evaluated. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CEC.2009.4983306 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
minimum free energy structure,rna secondary structure,efn2 model,enhanced model,stacking-energy thermodynamic model,evolutionary algorithm,enhanced thermodynamic model,rna secondary structure prediction,rna sequence,rna molecule,structure prediction technique,computational rna,native structure,gold,algorithm design and analysis,predictive models,proteins,macromolecules,free energy,shape,rna,thermodynamics,protein synthesis,accuracy,stacking,sensitivity,evolutionary computation,rna structure,structure function | RNA,Evolutionary algorithm,Computer science,Macromolecule,Evolutionary computation,Protein biosynthesis,Artificial intelligence,Machine learning,Nucleic acid secondary structure,Energy minimization,Stacking | Conference |
ISBN | Citations | PageRank |
978-1-4244-2959-2 | 1 | 0.37 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kay C. Wiese | 1 | 164 | 19.10 |
| Andrew G. Hendriks | 2 | 1 | 0.71 |