Background: RNARNA interaction plays an important role in the regulation of gene expression and cell
development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing
stable interactions with it. In the RNARNA interaction prediction problem, two RNA sequences are given as inputs
and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms
have been proposed to predict RNARNA interaction structure. However, most of them suffer from high
computational time.
Results: In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNARNA interaction. The
proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in
comparison to the other stateoftheart algorithms. In the proposed algorithm, each individual is a secondary structure
of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each
generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two
interacting RNAs by using crossover and mutation operations.
Conclusions: This algorithm is properly employed for joint secondary structure prediction. The results achieved on a
set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity
of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each
iteration is as efficient as the other approaches.
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