Monther Aldwairi, W. Alqarqaz, R. Duwairi
Abstract:
RiboNucleic Acid (RNA) molecules fold back over
themselves to form secondary structures which determine the
RNA?s functionality in living cells. RNA secondary structure
can be determined in laboratory by X-ray diffraction and
nuclear magnetic resonance (NMR) techniques. However, these
techniques are slow and expensive. Therefore, computational
approaches are used to predict the secondary structure of RNA
molecules. A new approach, RNA-SSP, for predicting RNA
secondary structure elements is proposed. It combines
computational approaches and machine learning classifiers to
predict individual structure elements using a new search
heuristic. The approach is implemented and tested for hairpin
loops and a methodology for extending the approach to predict
the remaining secondary structure elements is proposed. The
experiments showed a significant improvement in prediction
accuracy to 95% for stem regions and 80% for loops. The
overall weighted-average accuracy for predicting hairpin loop
sub-structure is 89% with a sensitivity of 85.29%.