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. Anyou Wang, a graduate of the University of California, Riverside, is passionate about computational biology, big data, and AI. 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 uncover noncoding RNAs (ncRNAs) as central players in all cancers and as evolutionary drivers of lifespan across animal species, challenging the traditional protein-centric paradigm. Additionally, Dr. Wang has developed innovative algorithms to systematically analyze all SARS-CoV-2 genomes, uncovering the virus’s evolutionary trajectory and origins, which contributed to the COVID-19 pandemic.