Technical University of Munich
Freising, Germany, Munich, Germany, Garching, Germany · 18 authors
Technical University of Munich (Freising, Germany, Munich, Germany, Garching, Germany): 18 authors and 10 papers in the de novo peptide sequencing catalog.
Departments
- Chair of Proteomics and Bioanalytics
- Computational Mass Spectrometry, School of Life Sciences
- Computational Mass Spectrometry, TUM School of Life Sciences
- Computational Molecular Medicine, School of Computation, Information and Technology
- Institute of Human Genetics, School of Medicine
- Institute of Human Genetics, School of Medicine and Health
- Institute of Virology, School of Medicine
- Munich Data Science Institute
- TUM School of Computation, Information and Technology
- TUM School of Life Sciences
Papers (10)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025, Journal of Proteome Research)
- Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides (2025, Molecular & Cellular Proteomics)
- Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025, bioRxiv)
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025, bioRxiv)
- Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra (2025, Journal of Proteome Research)
- Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra (2025, bioRxiv)
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024, Nature Communications)
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2023, bioRxiv)
- Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning (2019, Nature Methods)