Abstract
Systems biology is an endeavor to explore various interconnected biological processes as a system toward discovery in medical applications, drug discovery, bioengineering, and universal complex problems. However, the high complexity of biological systems makes it strenuous to understand systems biology comprising high-throughput, large-scale, and multi-view big data of numerous formats. In this context, artificial intelligence (AI) stretches its hands with different technologies, such as marker-passing algorithms, statistical inference, qualitative physics, text mining, machine learning, and deep learning. This chapter addresses many challenges in systems biology, particularly high-throughput imbalance multi-omics data, complex hierarchical biological networks, and drug discovery. Besides, it discusses how AI can transform the future of systems biology by solving these issues.
