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A Northwestern University and Buck Institute study published in Aging Cell finds that postmenopausal ovaries remain biologically active, continuing to remodel with age and sharing molecular profiles linked to ovarian cancer, challenging long-held assumptions about the ovary after menopause.
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.