Spectrum representation ablation study
benchmark
Spectrum representation ablation study: benchmark. MSc thesis ablation of how MS/MS peaks should be represented for a Transformer de novo sequencer. Holds a Casanovo-style encoder-decoder backbone fixed and varies only…
MSc thesis ablation of how MS/MS peaks should be represented for a Transformer de novo sequencer. Holds a Casanovo-style encoder-decoder backbone fixed and varies only the peak embedding: additive m/z-intensity, separated/concatenated m/z and intensity, precursor-normalised (relative) m/z, learnable relative m/z, zero-vector controls, and probability-aware embeddings fed with DpNovo’s signal-vs-noise peak probabilities. Finds that separating peak features beats the additive baseline, relative m/z adds a further gain, and the best result combines external signal probability with intensity and mass features: evidence that input representation, not just architecture, moves the needle on Transformer de novo accuracy.
| Kind | benchmark |
| Deep learning | yes |