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References from Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
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Requirements for prediction of peptide retention time in reversed-phase high-performance liquid chromatography: hydrophilicity/hydrophobicity of side-chains at the N- and C-termini of peptides are dramatically affected by the end-groups and location
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Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses
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Utility of retention prediction model for investigation of peptide separation selectivity in reversed-phase liquid chromatography: impact of concentration of trifluoroacetic acid, column temperature, gradient slope and type of stationary phase
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Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics
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Zika infection disrupts proteins involved in neurosensory system
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