Artificial Bee Colony Algorithm in Multi-omics Analysis: A Case Study

Multi-omics is an integrative approach comprising genomics, transcriptomics, proteomics, metabolomics, and epigenomics that provides an understanding of complex biological systems at various molecular levels. It demonstrates potential in designing personalized diagnostic and therapeutic strategies. Despite substantial benefits, multi-omics faces challenges in gene selection optimization, biomarker relevance validation, and method standardization that must be overcome to utilize its full potential. Artificial intelligence and nature-inspired algorithms offer several approaches to integrate multi-omics data and identify significant features. Swarm intelligence algorithms are nature-inspired algorithms favored by social insects such as honeybees. The Artificial Bee Colony (ABC) algorithm is inspired by the foraging behavior of honeybees which explores complex solution spaces and optimizes features for enhanced biomarker selection. The global optimization capacity of the ABC algorithm complements the complex nature of multi-omics which helps to reveal the relevant and unique feature subset enabling the diagnosis, prognosis, and therapeutic responses. This chapter includes a comprehensive case study examining the efficacy of ABC and its hybrid swarm optimization algorithms for molecular feature optimization in multi-omics analysis.