A particle swarm optimization solution for challenging planted(l, d)-Motif problem

In Bioinformatics, Planted (l, d)-Motif finding is an important and challenging problem, which has many applications. Generally, it is to locate recurring patterns in the promoter regions of co-expressed or co-regulated genes. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NP-complete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. These solutions are either “exact” or “approximate”. All the proposed exact solutions take exponential-time; they need more time to search for larger parameters l and d. The problems of Bioinformatics seldom need the exact optimum solution; rather what they need is robust, fast and near optimal solutions. Therefore, it is impractical to use an exact algorithm to search for large parameters of motifs in real biological dataset. In this paper, we have adopted the features of the Particle Swarm Optimization (PSO) with k-nearest neighbor algorithm to solve the Planted (l, d)-Motif Finding Problem. PSO is a global approximation optimization technique and has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have performed some experiments on synthetic data by increasing number of sequences and the length of the sequences for different (l, d)-Motifs for the following data sets: general instances (10, 2), (11, 2), (12, 3), (15, 4), (16, 5), (18, 6), (20, 7) (30, 11) and (40,15). Challenging instances: (9, 2), (11, 3), (13, 4), (15, 5), (20, 7), (30, 11), (40, 15) and finally, we have applied our proposed method for real biological sequences. From the experimental results we observe that the proposed algorithm is more efficient and accurate compared to existing approximation algorithms and even it works better for larger motif instances.