GNN
2 methods · 2023
GNN: Graph neural networks over the spectrum graph: they learn the path-scoring function that dynamic programming had to hand-code, which is what lets a path cross a fragment that was never observed.
Graph neural networks over the spectrum graph: they learn the path-scoring function that dynamic programming had to hand-code, which is what lets a path cross a fragment that was never observed.
The earliest of its 2 methods is Denovo-GCN (2023).
| Methods | 2 |
| Papers describing them | 2 |
| Authors | 8 |
| Active | 2023-04-05 to 2023-10-19 |
| Deep learning | 2 of 2 |
| Kinds | algorithm (2) |
| Acquisition | DDA (2) |
Methods (2)
Oldest first, by the paper that describes each one.
- Denovo-GCN (2023): GCN on spectrum graph
- GraphNovo (2023): Missing fragments
How they score
2 of the 2 have been run on denovo_benchmarks, which ranks 17 tools over 84 datasets. The family’s best median rank is 9.
- GraphNovo: median peptide-level average precision 0.671, median rank 9 of 17
- Denovo-GCN: median peptide-level average precision 0.538, median rank 13 of 17
Read these next to the rest of the field, not on their own: what the numbers mean.
Papers describing them (2)
- Denovo-GCN: De Novo Peptide Sequencing by Graph Convolutional Neural Networks (2023, Applied Sciences (MDPI), peer-reviewed)
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023, Nature Machine Intelligence, peer-reviewed)