Diffusion spectrum foundation model
adjacent · Diffusion
Self-supervised diffusion encoder for tandem mass spectra, trained by masked spectrum prediction on unlabelled data to give general-purpose spectrum embeddings transferable to downstream proteomics tasks instead of representations inherited from a model optimised only for de novo sequencing. Reaches an R² of 0.784 for precursor m/z at scale against a 0.923 baseline while learning compact, organised latent representations, a trade-off between compactness and predictive accuracy; precursor-charge and fragmentation-type classification stay near the majority-class baselines. Ablations indicate the masking and multi-loss objective drive the latent structure, and that the centroid loss needs modulation such as AdaLN to avoid penalising the learned embeddings.
| Kind | adjacent |
| Family | Diffusion |
| Deep learning | yes |
| Acquisition | DDA |