ArticleNature communications2025
DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Analysis Model for Infant Incubator Adverse Events Using Retrieval-Augmented Generation Combined With Dual-Adapter Fine-Tuning: Development and Evaluation Study.JMIR medical informatics · 2026Article
- Spectral Cruncher: A Visualization Tool Integrating Manual Curation, Ion-Intensity Prediction, and De Novo Tag Generation.Journal of the American Society for Mass Spectrometry · 2026Article
- DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis.Nature communications · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly pivotal in quantitative proteomics. In this study, we present DIA-BERT, a software tool that harnesses a transformer-based pre-trained artificial intelligence (AI) model for analyzing DIA proteomics data. The identification model was trained using over 276 million high-quality peptide precursors extracted from existing DIA-MS files, while the quantification model was trained on 34 million peptide precursors from synthetic DIA-MS files. When compared to DIA-NN, DIA-BERT demonstrated a 51% increase in protein identifications and 22% more peptide precursors on average across five human cancer sample sets (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma), achieving high quantitative accuracy. This study underscores the potential of leveraging pre-trained models and synthetic datasets to enhance the analysis of DIA proteomics.
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