Shanghai Artificial Intelligence Laboratory
Shanghai, China · 19 authors
Shanghai Artificial Intelligence Laboratory (Shanghai, China): 19 authors and 12 papers in the de novo peptide sequencing catalog.
Papers (12)
- AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
- MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry (2026, arXiv)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Accurate de novo sequencing of the modified proteome with OmniNovo (2025, arXiv)
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025, NeurIPS 2025)
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025, bioRxiv)
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025, ICML 2025)
- Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (2025, ICLR 2025)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
- RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing (2024, ICLR 2025)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024, bioRxiv)
- ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024, AAAI 2024)