ArticleMetabolites2020
QSRR Automator: A Tool for Automating Retention Time Prediction in Lipidomics and Metabolomics.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Exploration of the structural diversity and distribution pattern of gangliosides and sulfatides in mouse brain tissues and biomarkers for Parkinson's Disease.Lipids in health and disease · 2026Article
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Article
- AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.Frontiers in public health · 2025Review
- Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.Metabolites · 2024Article
- Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches.Molecules (Basel, Switzerland) · 2024Article
- Article
- User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules.Molecules (Basel, Switzerland) · 2022Article
- In-depth profiling of carboxyl compounds in Chinese Baijiu based on chemical derivatization and ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry.Food chemistry: X · 2022Article
- Probabilistic metabolite annotation using retention time prediction and meta-learned projections.Journal of cheminformatics · 2022Article
- Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water.Molecules (Basel, Switzerland) · 2022Article
- New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.Analytical chemistry · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The use of retention time is often critical for the identification of compounds in metabolomic and lipidomic studies. Standards are frequently unavailable for the retention time measurement of many metabolites, thus the ability to predict retention time for these compounds is highly valuable. A number of studies have applied machine learning to predict retention times, but applying a published machine learning model to different lab conditions is difficult. This is due to variation between chromatographic equipment, methods, and columns used for analysis. Recreating a machine learning model is likewise difficult without a dedicated bioinformatician. Herein we present QSRR Automator, a software package to automate retention time prediction model creation and demonstrate its utility by testing data from multiple chromatography columns from previous publications and in-house work. Analysis of these data sets shows similar accuracy to published models, demonstrating the software's utility in metabolomic and lipidomic studies.
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Registered trials
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