Our latest work on deep learning for metabolomics just appeared in Scientific Reports!

We introduce GEMNA, a deep learning framework for mass spectrometry–based metabolomics that uses graph and edge embeddings with anomaly detection. GEMNA outperforms traditional tools in untargeted studies, producing clearer clusters and improved biological insights. Published in Scientific Reports