Prime-DiffNovo

algorithm · Transformer (NAR)

Prime-DiffNovo: algorithm · Transformer (NAR). Non-autoregressive Transformer de novo peptide sequencer with a diffusion-based refinement step at inference time: the NAR pass drafts a full sequence in parallel, and…

Non-autoregressive Transformer de novo peptide sequencer with a diffusion-based refinement step at inference time: the NAR pass drafts a full sequence in parallel, and the diffusion loop iteratively corrects residue predictions to sharpen accuracy.

Kind algorithm
Deep learning yes
Family Transformer (NAR)

Data

What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.

Evaluated on

Reported comparisons (2)

The comparison tables this method’s own papers print, standardised: every value on a 0-1 scale, methods down the side, the measure and then the species across. These are numbers papers report about themselves and their baselines. They are not a leaderboard, and they do not compare across tables: each was produced by a different group, on the dataset named in its corner, with each baseline either retrained, run from released weights or quoted from another paper. Where the paper says which, it follows the method’s name (hover it for the sentence); most papers do not say. Bold is the best value in a column and underline the runner-up, our ranking rather than the paper’s own marks.

Table 1 (Nine-species · original (DeepNovo, 2017))

Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement, page 8: Peptide accuracy comparison of different de novo peptide sequencing methods across nine species on two benchmark datasets.

Nine-species benchmark
original (DeepNovo, 2017) 		Peptide accuracy
Apis mellifera Bacillus subtilis Candidatus Thiodiazotropha endoloripes Homo sapiens Methanosarcina mazei Mus musculus Saccharomyces cerevisiae Solanum lycopersicum Vigna mungo Weighted average
InstaNovo v1.1 0.526 0.629 0.444 0.506 0.571 0.509 0.602 0.615 0.613 0.565
InstaNovo+ v1.1 0.555 0.662 0.475 0.516 0.596 0.525 0.634 0.642 0.662 0.593
π-PrimeNovo 0.592 0.697 0.511 0.566 0.627 0.558 0.688 0.679 0.678 0.630
Prime-DiffNovo 0.607 0.716 0.526 0.572 0.644 0.566 0.698 0.690 0.709 0.645

Table 1 (Nine-species · revised (build not stated))

Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement, page 8: Peptide accuracy comparison of different de novo peptide sequencing methods across nine species on two benchmark datasets.

Nine-species benchmark
revised (build not stated) 		Peptide accuracy
Apis mellifera Bacillus subtilis Candidatus Thiodiazotropha endoloripes Homo sapiens Methanosarcina mazei Mus musculus Saccharomyces cerevisiae Solanum lycopersicum Vigna mungo Weighted average
InstaNovo v1.1 0.625 0.704 0.512 0.670 0.666 0.504 0.758 0.721 0.679 0.698
InstaNovo+ v1.1 0.646 0.731 0.545 0.687 0.691 0.523 0.779 0.743 0.704 0.723
π-PrimeNovo 0.602 0.702 0.549 0.704 0.681 0.537 0.671 0.699 0.731 0.682
Prime-DiffNovo 0.658 0.751 0.581 0.733 0.717 0.566 0.817 0.771 0.748 0.749

Paper describing it

Authors (3)

Xuan Yu, Kunyi Li, Lusheng Wang

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