Abstract
Predicting the secondary structure of RNA sequences has been proved quite a challenging research field for bioinformatics. Predicting structures that encapsulate the pseudoknot motif highlights why it is an NP-complete problem. In this setting, researchers focus on accurately predicting this motif and its variations by leveraging heuristic methodologies that converge while decreasing the prediction time. Any accurate heuristic does not add significant value when it involves an extended execution period, specifically considering lengthy sequences. In this work, we introduce a novel, time-efficient method that employs grammar attributes, parallel execution, and pruning techniques to create an efficient prediction tool that is helpful for biologists, bioengineers, and biomedical researchers. This version of the proposed framework features a pruning technique to reduce the search space of the grammar. It eliminates trees derived from corner-case conditions to reduce execution time by 33% regarding the grammar-based methodology and 43% regarding the brute-force approach without sacrificing the initial accuracy percentage.
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Acknowledgement
The research leading to the results presented in this article has received funding from the European Union’s funded Project PolicyCLOUD under grant agreement No. 870675.
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Makris, E., Kolaitis, A., Andrikos, C., Moulos, V., Tsanakas, P., Pavlatos, C. (2022). An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_15
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