Protein motifs retrieval by SS terns occurrences

World Competitive Contests (WCC) algorithm: A novel intelligent optimization algorithm for biological and non-biological problems

Since different sciences face lots of problems which cannot be solved in reasonable time order, we need new methods and algorithms for getting acceptable answers in proper time order. In the present study, a novel intelligent optimization algorithm, known as WCC (World Competitive Contests), has been proposed and applied to find the transcriptional factor binding sites (TFBS) and eight benchmark functions discovery processes. We recognize the need to introduce an intelligent optimization algorithm because the TFBS discovery is a biological and an NP-Hard problem. Although there are some intelligent algorithms for the purpose of solving the above-mentioned problems, an optimization algorithm with good and acceptable performance, which is based on the real parameters, is essential. Like the other optimization algorithms, the proposed algorithm starts with the first population of teams. After teams are put into different groups, they will begin competing against their rival teams. The highly qualified teams will ascend to the elimination stage and will play each other in the next rounds. The other teams will wait for a new season to start. In this paper, we’re going to implement our proposed algorithm and compare it with five famous optimization algorithms from the perspective of the following: the obtained results, stability, convergence, standard deviation and elapsed time, which are applied to the real and randomly created datasets with different motif sizes. According to our obtained results, in many cases, the WCC׳s performance is better than the other algorithms’.

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A new optimization algorithm which is named as WCC (World Competitive Contests) and is inspired by human sport rules is introduced.

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WCC is applied to TFBS (transcriptional factor binding sites) problem and eight benchmark functions.

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WCC is compared with the five known optimization algorithms.

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Experimental results show WCC’s remarkable performance relative to other optimization algorithms on eight benchmark functions and TFBS problem.

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WCC is implementable on the problems which can’t be solved in reasonable time order (NP problems).

A method of protein model classification and retrieval using bag-of-visual-features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods.