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Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity

Journal of proteome research, 2024-04, Vol.23 (4), p.1488-1494

2024

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Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments

Journal of proteome research, 2022-05, Vol.21 (5), p.1218-1228

2022

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Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach

Journal of proteome research, 2023-08, Vol.22 (8), p.2734-2742

2023

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Universal retention standard for peptide separations using various modes of high-performance liquid chromatography

Journal of Chromatography A, 2019-03, Vol.1588, p.163-168

2019

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DeepLC can predict retention times for peptides that carry as-yet unseen modifications

Nature methods, 2021-11, Vol.18 (11), p.1363

2021

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Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Nature methods, 2019-06, Vol.16 (6), p.509

2019

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Peptide retention time prediction

Mass spectrometry reviews, 2017-09, Vol.36 (5), p.615-623

2017

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AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Nature communications, 2022-11, Vol.13 (1), p.7238-7238, Article 7238

2022

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Retention Time Prediction for Glycopeptides in Reversed-Phase Chromatography for Glycoproteomic Applications

Analytical chemistry (Washington), 2019-11, Vol.91 (21), p.13360-13366

2019

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Predicting fragment intensities and retention time of iTRAQ‐ and TMTPro‐labeled peptides with Prosit‐TMT

Proteomics (Weinheim), 2022-10, Vol.22 (19-20), p.e2100257-n/a

2022

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MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

Nature communications, 2024-01, Vol.15 (1), p.661-661, Article 661

2024

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A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics

Proteomics (Weinheim), 2023-04, Vol.23 (7-8), p.e2200041-n/a

2023

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Data Dependent–Independent Acquisition (DDIA) Proteomics

Journal of proteome research, 2020-08, Vol.19 (8), p.3230-3237

2020

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Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning[S]

Molecular & cellular proteomics, 2019-10, Vol.18 (10), p.2099-2107

2019

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Improved LC–MS identification of short homologous peptides using sequence-specific retention time predictors

Analytical and bioanalytical chemistry, 2023-06, Vol.415 (14), p.2715-2726

2023

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Deep Learning in Proteomics

Proteomics (Weinheim), 2020-11, Vol.20 (21-22), p.e1900335-n/a

2020

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High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions

Analytical chemistry (Washington), 2023-05, Vol.95 (19), p.7495-7502

2023

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Applicability of linear and nonlinear retention-time models for reversed-phase liquid chromatography separations of small molecules, peptides, and intact proteins

Journal of separation science, 2016-04, Vol.39 (7), p.1249-1257

2016

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Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Nature communications, 2021-05, Vol.12 (1), p.3237-3237, Article 3237

2021

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Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography: Data Collection Methods and Features of Additive and Sequence-Specific Models

Analytical chemistry (Washington), 2017-05, Vol.89 (10), p.5526-5533

2017

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Intensity and retention time prediction improves the rescoring of protein‐nucleic acid cross‐links

Proteomics (Weinheim), 2024-04, Vol.24 (8), p.e2300144-n/a

2024

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Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization

Molecules (Basel, Switzerland), 2020-07, Vol.25 (13), p.3085

2020

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Inside Front Cover: Deep Learning in Proteomics

Proteomics (Weinheim), 2020-11, Vol.20 (21-22), p.n/a

2020

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Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions

Journal of Chromatography A, 2019-08, Vol.1598, p.92-100

2019

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Peptide separation selectivity in proteomics LC‐MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion‐pairing modifiers

Journal of separation science, 2020-10, Vol.43 (20), p.3830-3839

2020

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Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments

Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach

Universal retention standard for peptide separations using various modes of high-performance liquid chromatography

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Peptide retention time prediction

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Retention Time Prediction for Glycopeptides in Reversed-Phase Chromatography for Glycoproteomic Applications

Predicting fragment intensities and retention time of iTRAQ‐ and TMTPro‐labeled peptides with Prosit‐TMT

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics

Data Dependent–Independent Acquisition (DDIA) Proteomics

Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning[S]

Improved LC–MS identification of short homologous peptides using sequence-specific retention time predictors

Deep Learning in Proteomics

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions

Applicability of linear and nonlinear retention-time models for reversed-phase liquid chromatography separations of small molecules, peptides, and intact proteins

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography: Data Collection Methods and Features of Additive and Sequence-Specific Models

Intensity and retention time prediction improves the rescoring of protein‐nucleic acid cross‐links

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization

Inside Front Cover: Deep Learning in Proteomics

Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions

Peptide separation selectivity in proteomics LC‐MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion‐pairing modifiers

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