Autoregressive LSTM and Transformer models achieve 98% top-1 accuracy predicting next eluting m/z bin from prior sequence features in lipidomics data across cohorts.
Metabo- lite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network- 32 ing.Nature Communications, 13(1), 2022
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The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics
Autoregressive LSTM and Transformer models achieve 98% top-1 accuracy predicting next eluting m/z bin from prior sequence features in lipidomics data across cohorts.