Name
Title | ||
|---|---|---|
The significance of thermodynamic models in the accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing |
Abstract | ||
|---|---|---|
Ribonucleic acid, a single stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is key to their function, algorithms for the prediction of RNA structure are of great value. This paper discusses significant improvements made to SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). One major improvement is the incorporation of a sophisticated thermodynamic model (efn2). This model is used by mfold to rank sub-optimal structures, but cannot be used directly by mfold during the structure prediction. Experiments on eight individual known structures from four RNA classes (5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA and 16S rRNA) were performed. The data demonstrate the robustness and the effectiveness of our improved prediction algorithm. The new algorithm shows results which surpass the dynamic programming algorithm mfold in terms of prediction accuracy on all tested structures. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424976 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
DNA,biology computing,dynamic programming,molecular biophysics,simulated annealing,RNA molecules,RNA secondary structure prediction,RNA strand,SARNA-Predict,biological systems,dynamic programming algorithm,homeostatic processes,linear molecule,living organisms,permutation-based simulated annealing,ribonucleic acid,thermodynamic models | Simulated annealing,RNA,23S ribosomal RNA,Nucleic acid structure,Computer science,Molecular biophysics,Intron,Artificial intelligence,5S ribosomal RNA,Base pair,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-1340-9 | 8 | 0.49 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Herbert H. Tsang | 1 | 92 | 19.08 |
| Kay C. Wiese | 2 | 164 | 19.10 |