In Proteomics
Accurate retention time 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 retention time 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. This article is protected by copyright. All rights reserved.
Pham Thang V, Nguyen Vinh V, Vu Duong, Henneman Alex A, Richardson Robin A, Piersma Sander R, Jimenez Connie R
2023-Mar-12
DIA-MS, Deep learning, retention time prediction, spectral library, transformer architecture
