MS-BioAP: A Pipeline for biomarker analysis based on data independent acquisition mass spectrometry.

In recent years, with the rapid development of mass spectrometry, there are more and more cases of finding biomarkers by proteomics for clinical diagnosis and treatment, and artificial intelligence is gradually replacing traditional statistical learning methods, but the process of finding biomarkers is not yet uniform and perfect, and there are gaps in validation. With this aim, this study proposes a pipeline for the analysis of biomarkers based on data independent acquisition(MS-BioAP). MS-BioAP includes three modules: mass spectrometry protein identification module, differential protein analysis module, and classification model building module. Combining statistical learning and artificial intelligence methods to complete the biomarker search, we also propose a target association scoring algorithm(TAScore), use the large amount of prior knowledge in the knowledge graph to complete the biomarker validation, and create a classification model based on the selected biomarkers. We conducted experiments using a clinical stress response mass spectrometry dataset and the final selected biomarkers achieved an accuracy of 1.0 in the classification model with both high sensitivity and specificity. The analytical pipeline is complete and comprehensive, with clinical implications. In the future, we will continue to optimize the analytical pipeline and validate this analytical pipeline with additional datasets.