ArticleAnalytical chemistry2025
Active Learning Improves Ionization Efficiency Predictions and Quantification in Nontargeted LC/HRMS.
Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A Novel Chemical-Space-Dependent Strategy for Compound Selection in Non-target LC-HRMS Method Development Using Physicochemical and Structural Data.Environmental science & technology letters · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is frequently employed in nontargeted screening (NTS) due to its high selectivity and sensitivity. However, data interpretation is challenging since the number of chemical standards available for quantification is limited and the response of the chemicals vastly differs depending on their structure and analysis conditions. Therefore, machine learning (ML) models have been utilized to predict ionization efficiency (
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.