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.
Roasmi: accelerating small molecule identification by repurposing retention data.Journal of Cheminformatics, 17(1), 2025
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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.