Spectrometry Based Proteomics

Spectrometry-based proteomics aims to identify and quantify proteins within complex biological samples using mass spectrometry data. Current research heavily utilizes deep learning, particularly transformer-based models and contrastive learning approaches, to improve the accuracy and speed of peptide sequencing *de novo* and database searching, addressing challenges like data-independent acquisition and post-translational modification identification. These advancements are crucial for accelerating proteome analysis, enabling more comprehensive studies of biological processes and facilitating personalized medicine through faster and more accurate protein profiling. Furthermore, research is exploring federated learning to enable collaborative analysis of sensitive proteomics data while preserving patient privacy.