The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

Abstract

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology

“…By supporting the utilization of AI, ASM can contribute to the refinement and expansion of ’omics studies, ushering in a new era of insight and discovery ( 63 , 64 ). The host-microbe community can learn from other fields that are already using AI as a discovery generation method ( 65 , 66 ), including the possible use of AI for diagnosis ( 67 ) or prognosis ( 68 ) of infectious diseases. Finally, ASM can lead discussions to establish a set of parameters for benchmarking the accuracy of computational models for host-microbe interaction studies and for promoting their continued refinement.…”