Open access
Aug 2026
Generalizable cancer detection from ultra-low-pass WGS via deep contextual modeling of cfDNA sequences
Fragmentia-AI™ WGS, a mutation-calling-independent framework that uses a transformer-based multiple-instance learning architecture with sequential fine-tuning across tumor fraction (TF) strata to extract latent cancer-associated signals from ULP-WGS data, enables robust cancer detection and clinically meaningful risk stratification from highly sparse cfDNA sequencing data.
Yang Xu, Song Wang, Guofeng Sun et al.
· Molecular Biomedicine · 0 citations