Artificial Intelligence-Driven Multilayer Network Analysis in Systems Biology: From Big Data Sourcing to Trendy Pattern Recognition

Artificial intelligence (AI) fosters deciphering complex relationships in biological data science. Integrating AI with systems biology opens new avenues for comprehensive gene regulatory network analysis, enhancing our understanding of gene regulatory systems at a multilayer level. Moreover, AI techniques and intense learning excel in uncovering latent complex relationships among heterogeneous datasets, making them a powerful tool for processing diverse biological and clinical data types. By imitating the nervous system's functional mechanisms, AI enhances cognitive potential in pattern recognition tasks, enabling the recognition of intertwined instructions and paving the way for intelligent machines with high mental abilities. Integrating complex networks, image analysis, signal processing, and AI in systems biology research offers unprecedented scientific and technological advancements, allowing for a holistic understanding of multilayer biological entities. Within this introductory framework, this Chapter's research question delves into AI's potential to revolutionize systems biology for gene regulation. Using multilayer network analysis, cognitive potential enhancement, and multivariate biological data processing, AI is poised to make significant contributions, and its network-driven scalability extends to biotech startups and business evaluations, backing economic sustainability. Integrating complex networks and AI in systems biology research is a step forward and a leap into unprecedented scientific and technological advancements. This integration offers a holistic understanding of multilayer biological entities, which benefits academics and practitioners.