Theory and perspective: Big data and artificial intelligence accelerate the pursuit of a universal cancer principle—opportunities and challenges

Although numerous cancer-associated mechanisms are reported on a daily basis, the establishment of a unifying theoretical framework capable of explaining and ultimately eradicating all cancers remains unresolved. Emerging advances in big data analytics and artificial intelligence have positioned noncoding RNAs as central, universal regulators of oncogenesis, thereby challenging the long-standing protein-centric paradigm. In contrast to proteins, noncoding RNAs exhibit dynamic and context dependent evolutionary trajectories rather than conserved patterns, and they remain insufficiently defined at both functional and structural levels. The lack of a comprehensive and systematic framework for their investigation significantly impedes progress toward a universal cancer theory. While big data and AI methodologies provide unprecedented opportunities to accelerate discovery in this domain, substantial challenges remain, including the generation of high-quality, large-scale biological datasets and the development of computational algorithms capable of capturing biological complexity. Addressing these barriers will necessitate the introduction of transformative theoretical constructs, novel algorithmic strategies, and advanced technological platforms at the interface of computational biology, big data, and AI.

Graduated from the University of California, Riverside, Dr. Wang is passionate about computational biology, big data, and AI  (combai.org). He develops computational algorithms to extract fundamental insights from large-scale biological data. He is a pioneer in utilizing the world’s largest database to discover that noncoding RNAs (ncRNAs) have their own functional system distinctive from  protein counterpart, which help to explain how ncRNAs act as key players in various physiological states. Dr. Wang further uncover  ncRNAs as primary players in all cancers and as evolutionary drivers of lifespan across animal species, challenging the traditional protein-centric paradigm. Additionally, he has developed innovative algorithms to systematically uncover SARS-CoV-2  evolutionary trajectory and origins, which contributed to the COVID-19 pandemic.