GraphNovo dataset and checkpoint
deposit · DDA · 1 version · 10 papers
GraphNovo’s training data published in the same DOI as its model weights, so the address is not data alone.
| Kind | deposit |
| Acquisition | DDA |
GraphNovo’s training data published in the same DOI as its model weights, so the address is not data alone.
Versions
as deposited
released 2023-06-07
Where it lives:
- DOI · 10.5281/zenodo.8000316
Trained on by (5)
- False discovery rate control for trustworthy AI-based de novo peptide sequencing (2026) preprint as deposited
- LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) preprint version not stated
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) ML conference version not stated
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) preprint version not stated
- Transforming de novo peptide sequencing by explainable AI (2024) preprint as deposited
Evaluated on by (10)
- False discovery rate control for trustworthy AI-based de novo peptide sequencing (2026) preprint as deposited
- LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) preprint version not stated
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026) peer-reviewed version not stated
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) ML conference version not stated
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025) preprint as deposited
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) preprint version not stated
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) peer-reviewed as deposited
- Transforming de novo peptide sequencing by explainable AI (2024) preprint as deposited
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) preprint as deposited
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) peer-reviewed as deposited
Methods on these papers (7)
- FDR control for AI-based de novo sequencing post-processor
- GraphNovo algorithm
- LIPNovo algorithm
- LIPNovo+ algorithm
- RNovA algorithm
- π-PrimeNovo algorithm
- π-xNovo algorithm
Taken from the describing links only, so a paper that merely ran a tool on this data does not make that tool a method of it.