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.
Qcomics: Recommendations and guidelines for robust, easily implementable and reportable quality control of metabolomics data.Analytical Chemistry, 96(3):1064–1072, 2024
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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.