Deep Learning for Proteomics-Based Cancer Classification and Prognosis
Date:
Presented collaborative research on AI-driven proteomics analysis for cancer classification and prognosis prediction. Introduced deep learning frameworks that integrate mass spectrometry data, clinical information, and multi-omics profiles for personalized cancer medicine. Discussed novel attention mechanisms for identifying prognostic protein biomarkers and pathway-level features. Demonstrated applications in thyroid cancer, medullary carcinoma, and other malignancies. Covered the development of interpretable models that provide biological insights into cancer mechanisms while achieving superior predictive performance compared to traditional statistical methods.