DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics

ABSTRACT: This project provides a large-scale training dataset for DDA-BERT, an end-to-end transformer-based deep learning model developed for PSM rescoring in data-dependent acquisition (DDA) proteomics. The dataset comprises curated PSMs generated using established database search engines, including FragPipe, Sage, and AlphaPept, and is intended to support the training of the DDA-BERT model.

INSTRUMENT(S):

ORGANISM(S): Rattus Norvegicus (rat) Homo Sapiens (human) Escherichia Coli Caenorhabditis Elegans Bacillus Cereus Bacillus Subtilis Saccharomyces Cerevisiae (baker's Yeast) Chlamydomonas Reinhardtii Arabidopsis Thaliana (mouse-ear Cress) Glycine Max Mus Musculus (mouse)

SUBMITTER: Jun A  

LAB HEAD: Tiannan Guo

PROVIDER: PXD072999 | Pride | 2026-08-24

REPOSITORIES: Pride