| Institution: | 1. Amsterdam UMC, location Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Medical Oncology, Amsterdam, The Netherlands;2. University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam;3. Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, Utrecht, The Netherlands;4. Amsterdam UMC, location Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Medical Oncology, Amsterdam, The Netherlands
Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands;5. Netherlands eScience Center, The Netherlands |
| 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. |
