A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics.
Proteomics
; 23(7-8): e2200041, 2023 04.
Article
em En
| MEDLINE
| ID: mdl-36906835
ABSTRACT
Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.
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Base de dados:
MEDLINE
Assunto principal:
Biblioteca de Peptídeos
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Proteômica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article