Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

ABSTRACT: We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would benefit greatly from understanding the underlying artificial intelligence decision process, which currently remains unclear. Here, we describe an interpretable framework to explain the classifier's decisions. We show that functional genomic regions of various sizes are predominantly employed to distinguish between different tumor classes, ranging from enhancers and CpG islands to large-scale heterochromatic domains. We detect a high degree of genomic redundancy, with many genes distinguishing individual tumor classes, explaining the robustness of

SUBMITTER: Benfatto S 

PROVIDER: S-EPMC11842776 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature