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Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Prediction of peptide retention times in reversed-phase high-performance liquid chromatography II. Correlation of observed and predicted peptide retention times factors and influencing the retention times of peptides
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An Automated Multidimensional Protein Identification Technology for Shotgun Proteomics
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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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Prediction of Chromatographic Retention and Protein Identification in Liquid Chromatography/Mass Spectrometry
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Quantitative Prediction of logk of Peptides in High‐Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine.
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Modeling and prediction of retention behavior of histidine‐containing peptides in immobilized metal‐affinity chromatography
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Comprehensive comparison of eight statistical modelling methods used in quantitative structure–retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome
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