Theory and perspective: How big data and AI accelerate the discovery of a universal cancer principle from the noncoding RNA regime

Title : Theory and perspective: How big data and AI accelerate the discovery of a universal cancer principle from the noncoding RNA regime

Abstract:

Numerous cancer-related mechanisms are reported daily, yet a universal theory for eradicating all cancers remains elusive. Fortunately, recent advances in big data and AI have identified noncoding RNAs as central players in all cancers, challenging the conventional protein-centric view. However, unlike proteins, noncoding RNAs evolve dynamically rather than following conserved patterns. Moreover, they remain poorly defined, and no systematic framework exists for their study, hindering the progress toward universal cancer theory. Although big data and AI hold promise for accelerating this discovery, significant challenges persist in biological data collection and computational algorithm development. Overcoming these barriers requires groundbreaking new scientific concepts, algorithms, and technologies in big data and AI.

Biography:

Dr. Wang graduated from the University of California, Riverside, and is passionate about computational biology, big data, and AI. He develops advanced algorithms to extract fundamental insights from large-scale biological data. As a pioneer in mining the world’s largest biological database, he discovered that noncoding RNAs (ncRNAs) operate through a functional system distinct from proteins. This breakthrough helps explain how ncRNAs play a central role in various disease mechanisms and immune responses to environmental fluctuations. Dr. Wang further identified ncRNAs as the primary drivers in all cancers and key determinants of lifespan across animal species, challenging the long-standing protein-centric paradigm. He also developed novel computational tools to trace the evolutionary trajectory and origin of SARS-CoV-2, contributing to the understanding of the COVID-19 pandemic.