SeqNovo
algorithm · Transformer (AR)
Seq2Seq for IoMT
| Kind | algorithm |
| Deep learning | yes |
| Acquisition | DDA |
| Family | Transformer (AR) |
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.
Trained on
- ProteomeTools (version not stated)
Evaluated on
- ProteomeTools (version not stated)
Reported comparison
The comparison table 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.
TABLEV
SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq, page 10: THE BEST RESULTS(ACCURACY) OF FOUR MODELS ON TESTSET
| ProteomeTools | Peptide accuracy | Amino acid accuracy |
|---|---|---|
| SeqNovo plain Seq2Seq baseline | 0.3093 | 0.7852 |
| SeqNovo MLP | 0.3226 | 0.7865 |
| SeqNovo attention | 0.3317 | 0.7930 |
| DeepNovo · retrained | 0.2420 | 0.8090 |
In the paper: column peptide accuracy: the original table bolded SeqNovo plain Seq2Seq baseline (30.93), SeqNovo MLP (32.26), SeqNovo attention (33.17).
Paper describing it
- SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq (2023, IEEE Journal of Biomedical and Health Informatics, peer-reviewed)