A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
Nature Biotechnology39(4), 462–471 (Apr 2021)
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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.
MetaboKG supplies a transformation workflow, semantic model based on PROV-O/SIO and domain ontologies, and Universal Annotation Identifier to enable graph-based integration and SPARQL queries over 680 GNPS molecular networking results.
citing papers explorer
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
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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.
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MetaboKG: An Analysis-centric Knowledge Graph Framework for Untargeted Metabolomics
MetaboKG supplies a transformation workflow, semantic model based on PROV-O/SIO and domain ontologies, and Universal Annotation Identifier to enable graph-based integration and SPARQL queries over 680 GNPS molecular networking results.