A diffusion decoder with DINOISER loss raises amino acid recall from 0.081 to 0.454 in Casanovo, while peptide precision and coverage stay at 0 and predicted sequences are much longer than the true peptides.
Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model
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Diffusion Decoding for Peptide De Novo Sequencing
A diffusion decoder with DINOISER loss raises amino acid recall from 0.081 to 0.454 in Casanovo, while peptide precision and coverage stay at 0 and predicted sequences are much longer than the true peptides.