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Clustering of biological datasets proved to reveal a lot of significant insights into the medical and biological research. It is an important step towards drug design, vaccine discovery, disease diagnosis, and more. The current trend in biological and medical research is to combine more than one dataset, referred to as multi-omics, related to a specific problem, then perform the clustering or the analysis. The insights we gain for a particular biological problem or disease are double using multi-omics rather than using one dataset. It is like investigating a problem from many dimensions rather than using one dimension. On the other hand, the difficulty of clustering is increased. Another tricky problem in data clustering is determining the best number of clusters used by a clustering algorithm. Due to the big success of metaheuristics in solving the automatic clustering problems, we propose in this paper a new hybrid method that utilizes two powerful metaheuristics algorithms, the Binary Differential Evolution and Marine Predators Algorithm, to perform automatic clustering on multi-omics datasets. Our proposal's performance is investigated upon eight multi-omics datasets from TCGA, and it is compared with four recent and powerful metaheuristics. The used performance metrics are clustering quality and execution time. The experimental results show that the proposed algorithm not only outperformed its competitors in terms of clustering quality, it also only needed a third of the execution time that of its fastest competitor. Moreover, the statistical analysis shows that the obtained results are statistically significant. Accordingly, the proposed method can be considered as an efficient clustering method for multi-omics datasets.

A New Hybrid Clustering Method of Binary Differential Evolution and Marine Predators Algorithm for Multi-omics Datasets