Publications (2)0 Total impact
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ABSTRACT: The RNA molecule is substantiated to play important functions in living cells. The class of RNA with pseudoknots, has essential roles in designing remedies for many virus diseases in therapeutic domain. These various useful functions can be inferred from RNA secondary structure with pseudoknots. Many computational intensive efforts have been emerged with the aim of predicting the pseudoknotted RNA secondary structure. The computational approaches are much promising to predict the RNA structure. The reason behind this is that, the experimental methods for determining the RNA tertiary structure are difficult, timeconsuming and tedious. In this paper, we introduce ABCRna, a novel method for predicting RNA secondary structure with pseudoknots. This method combines heuristic-based KnotSeeker with a thermodynamic programming model, UNAFold. ABCRna is a hybrid swarm-based intelligence method inspired by the secreting honey process in natural honey-bee colonies. The novel aspect of this method is adapting Case-Based Reasoning (CBR) and knowledge base, two prominent Artificial Intelligence techniques. They are employed particularly to enhance the quality performance of the proposed method. The CBR provides an intelligent decision, which results more accurate predicted RNA structure. This modified ABCRna method is tested using different kinds of RNA sequences to prove and compare its efficiency against other pseudoknotted RNA predicted methods in the literature. The proposed ABCRna algorithm performs faster with significant improvement in accuracy, even for long RNA sequences.
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ABSTRACT: Problem statement: Finding an accurate RNA structural alignment from primary sequence due to it is time consuming and computationally NP-hard problem is a major bioinformatics challenge. According to our investigation majority of current researches were concerned on achieving faster execution time, improving space complexity and better cache management. Recently one research introduced cache-efficient Chip Multiprocessor (CMP) algorithms with good speed-up to exploit parallelism in detection the critical path length. Our contribution in this article was a comprehensive survey of methods for solving RNA secondary structure prediction with Pseudoknots (PK) and sequence alignment in bioinformatics. The aim was to highlight the challenges related issues which would provide sufficient information to assist the new coming researchers in this field as well as a good reference guide for bioinformatics professionals. Approach: We computed various algorithms that predicted an RNA molecules secondary structure from primary sequence, without pseudoknots from one side and pseudoknotted RNA secondary structure in the other side. Furthermore, we also reviewed and compared in two tables the methods that developed for RNA structural predictions. Results: Our findings of this survey confirmed that Dynamic Programming (DP) method via CMP algorithms can be used to predict the RNA secondary structure with simple PK and it gives good results. Conclusion: The methods for predicting RNA's structural are coming in two groups: Firstly, pseudoknotted RNA structural problem is computationally complex and secondly, common methods significantly gave not accurate enough results for predicting pseudoknotted RNA.
Universiti Sains Malaysia
Penang, Penang, Malaysia
- School of Computer Science