AI improves cancer biomarker discovery and enables remote homology inference

Abstract

This thesis centers around cancer detection, where AI-driven methods enhance diagnostic accuracy and deepen our understanding of underlying mechanisms through the following key elements. The first element is precision diagnostics, which enhances current clinical methods for pancreatic cancer by using machine learning to integrate RNA variants extracted from peripheral blood, significantly improving diagnostic accuracy. The second element explores the potential for pancancer diagnosis by identifying biomarkers shared across different cancer types. To achieve this, a network algorithm is developed to identify a universal 50-biomarker signature that effectively predicts outcomes in 10 cancers by incorporating biomarker connectivity into the analysis. Third, protein structure analysis adds interpretability by linking protein sequences to their 3D structures, highlighting how structures and functions can remain conserved despite significant sequence variations, or vice versa. This aspect also explains the impact of AI in protein structure prediction, exemplified by the largest study of its kind, which builds a phylogenetic structural tree for the Rad52 superfamily—a known cancer target—by examining conserved regions vital for DNA repair. Finally, a new method for homology inference using protein secondary structures enables the rapid search of millions of proteins to identify shared structural features. Homology inference could be further exploited in the future to find common structural features across biomarkers of different cancer types, enhancing the improvement of our pan-cancer signatures