A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models
peer-reviewed · Scientific Data · 2024
peer-reviewed · Scientific Data · 2024. Bo Wen et al. Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large…
| Date | 2024-11-08 |
| Type | peer-reviewed |
| Venue | Scientific Data |
| Publisher | Springer Nature |
| Contribution | benchmark |
| DOI | 10.1038/s41597-024-04068-4 |
| Citations (OpenAlex) | 10 |
| Venue 2-year citedness | 5.17 |
Preprint version: A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (2024-08-30, ChemRxiv)
Abstract
Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from nine different species. The dataset is based on a previously described benchmark but has been re-processed to ensure consistent data quality and enforce separation of training and test peptides.
Methods and tools
- 9-species multi-species benchmark: Multi-species PSM benchmark for ML training
Cites (18)
- ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024) crossref
- AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024) crossref
- Bidirectional de novo peptide sequencing using a transformer model (2024) both
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024) crossref
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024) crossref
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) crossref
- Accurate de novo peptide sequencing using fully convolutional neural networks (2023) both
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) crossref
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023) semanticscholar
- BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method (2023) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023) crossref
- DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers (2022) crossref
- DePS: An improved deep learning model for de novo peptide sequencing (2022) crossref
- De novo mass spectrometry peptide sequencing with a transformer model (2022) crossref
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) both
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) crossref
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) both
- De novo peptide sequencing by deep learning (2017) both
Cited by (5)
- Learning Fragmentation Physics or Exploiting Sequence Priors? Benchmarking Bias in Deep Learning Models for De Novo Peptide Sequencing (2026) both
- π-MSNet: A billion-scale, AI-ready living proteomics data portal (2026) crossref
- XA-Novo: an accurate and high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures (2026) crossref
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025) crossref