Bio-Inspired Metaheuristic Framework for DNA Motif Discovery Using Hybrid Cluster Based Walrus Optimization

Motifs are short, recurring sequence elements with biological significance within a set of nucleotide sequences. Motif discovery is the problem of finding these motifs. The problem of motif discovery has become an important problem in the field of Bioinformatics since, it finds its applications in Drug discovery, Environmental Health Research, and early Detection of Diseases by finding anomalies in gene sequences. Motif discovery is a challenging job in bioinformatics since it is NP-hard and cannot be solved within an exact time. In this study, we have proposed Hybrid Cluster based Walrus Optimization algorithm (HCWaOA) to solve the problem of motif discovery. The accuracy and efficiency of the proposed algorithm are improved using a hybrid approach. The population is initialized using Random Projection technique to generate a meaningful solution space. Then, k-means clustering is used to group similar solutions. Lastly, a population-based metaheuristic algorithm, Walrus optimization technique, is applied on each of the clusters to find the best motif. The proposed Hybrid Cluster-based Walrus Optimization algorithm (HCWaOA) is tested on both simulated and real biological datasets. The performance of HCWaOA is compared with benchmark algorithms like MEME, AlignCE and other meta-heuristics algorithms. The results of the proposed algorithm are found to be stable with a precision of 92%, a recall of 93% and an F-score of 93%. The proposed HCWaOA is tested using biological cancer-causing BARC and CTCF datasets to identify cancer causing motifs. Results show that incorporating clustering to initial solution space results in optimal solutions within a fewer iteration. The results of HCWaOA are compared with other popular motifs discovery algorithms and found to be stable.