Shanghai Innovation Institute
Shanghai, China · 2 authors
Shanghai Innovation Institute (Shanghai, China): 2 authors and 14 papers in the de novo peptide sequencing catalog.
Papers (14)
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics (2026, Nature Methods)
- Accurate de novo sequencing of the modified proteome with OmniNovo (2025, arXiv)
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025, NeurIPS 2025)
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025, arXiv)
- Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025, ICML 2025)
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025, ICML 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, arXiv)
- Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025, arXiv)
- Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (2025, OpenReview)
- 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, OpenReview)
- 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)