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Scientists used mass spectrometry-based proteomics to analyze ancient Egyptian artifacts, discovering the use of animal-derived proteins like cow glue in adhesives and paints.
An LLM-agent framework is presented for large-scale analysis of cross-tissue protein co-abundance networks, identifying conserved clusters and generating mechanistic hypotheses for disease mechanisms.
This paper systematically benchmarks classical machine learning models (Random Forest, XGBoost, etc.) for ER status prediction using multi-omics data from TCGA-BRCA, finding that RNA expression provides the strongest predictive signal and that Random Forest achieves 90.3% balanced accuracy in the integrated multi-omic setting.