Mass Spectrometry-Based Proteomics for Cancer Subtyping and Prognosis Prediction
Date:
Advanced research presentation on leveraging mass spectrometry proteomics data for cancer classification and prognosis. Presented deep learning frameworks that process raw DIA (Data-Independent Acquisition) mass spectrometry data for end-to-end prediction. Discussed novel architectures for handling peptide-level features, protein-protein interaction networks, and pathway enrichment simultaneously. Demonstrated superior performance in thyroid cancer subtyping, medullary carcinoma prognosis, and pan-cancer biomarker discovery. Covered collaborative work with clinical institutions validating AI models on prospective cohorts. Explored the potential of integrating proteomics with genomics and clinical data for personalized cancer treatment strategies.