Inverse RNA folding solution based on multi-objective genetic algorithm and Gibbs sampling method

In living systems, RNAs play important biological functions. The functional form of an RNA frequently requires a specific tertiary structure. The scaffold for this structure is provided by secondary structural elements that are hydrogen bonds within the molecule. Here, we concentrate on the inverse RNA folding problem. In this problem, an RNA secondary structure is given as a target structure and the goal is to design an RNA sequence that its structure is the same (or very similar) to the given target structure. Different heuristic search methods have been proposed for this problem. One common feature among these methods is to use a folding algorithm to evaluate the accuracy of the designed RNA sequence during the generation process. The well known folding algorithms take O(n3) times where n is the length of the RNA sequence. In this paper, we introduce a new algorithm called GGI-Fold based on multiobjective genetic algorithm and Gibbs sampling method for the inverse RNA folding problem. Our algorithm generates a sequence where its structure is the same or very similar to the given target structure. The key feature of our method is that it never uses any folding algorithm to improve the quality of the generated sequences. We compare our algorithm with RNA-SSD for some biological test samples. In all test samples, our algorithm outperforms the RNA-SSD method for generating a sequence where its structure is more stable

Cigarette smoking and its relationship with perceived familial support and religiosity of university students in Tabriz

Objective: The goal of the present study was to assess the prevalence of cigarette smoking and its relationship to other risk taking behaviors, perceived familial support and religiosity among college students in Tabriz, Iran . Method: In this study, 1837 randomly selected students participated and completed a self-administered questionnaire inquiring demographic characteristics, risk taking behaviors, Aneshensel and Sucoff's 13-items one-dimensional perceived Parental support scale and 28 - items Kendler's general religiosity scale. Results: In general, 15.8 of the students were cigarette smokers. The results indicated that being male (OR = 3.21), living alone or with friends (OR = 2.00), having a part-time job (OR = 1.98), alcohol consumption during the past 30 days (OR = 3.67), hookah use (OR = 5.23), substance abuse (OR = 1.69), familial support (OR = 0.97) and religiosity (OR = 0.98) have statistically significant relationships with cigarette smoking . Conclusion: Our study represents the co-occurrence of risky behaviors. Cultural context in the traditional communities seems to show the crucial role of familial support and religiosity traits with the female gender as predictive factors to not smoke cigarette and perform other risky behaviors

Genetic algorithm solution for double digest problem

The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data

Efficient Generation, Ranking, and Unranking of (k, m)-Ary Trees in B-Order

In this paper, we present a new generation algorithm with corresponding ranking and unranking algorithms for (k, m)-ary trees in B-order. (k, m)-ary tree is introduced by Du and Liu. A (k, m)-ary tree is a generalization of k-ary tree, whose every node of even level of the tree has degree k and odd level of the tree has degree 0 or m. Up to our knowledge no generation, ranking or unranking algorithms are given in the literature for this family of trees. We use Zaks’ encoding for representing (k, m)-ary trees and to generate them in B-order. We also prove that, to generate (k, m)-ary trees in B-order using this encoding, the corresponding codewords should be generated in reverse-lexicographical ordering. The presented generation algorithm has a constant average time and O(n) time complexity in the worst case. Due to the given encoding, both ranking and unranking algorithms are also presented taking O(n) an