ArticleJournal of proteome research2022
Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry.
Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Integrating Low-Voltage Microfluidics With On-Chip Electrospray Ionization for Miniaturized Protein Analysis.Small methods · 2026Review
- A simple and efficient approach for preparing cationic coating with tunable electroosmotic flow for capillary zone electrophoresis-mass spectrometry-based top-down proteomics.Analytica chimica acta · 2024Article
- Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications.Analytical methods : advancing methods and applications · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (
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