ArticleJournal of chemometrics2015
Discovery of False Identification Using Similarity Difference in GC-MS based Metabolomics.
Article in Journal of chemometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Odour Profiling for Animal Management and Behavioural Manipulation: An Overview of the Tools and Applications.Journal of chemical ecology · 2026Review
- Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.Metabolites · 2023Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
- Article
- Optimization of the Data Treatment Steps of a Non-targeted LC-MS-Based Workflow for the Identification of Trace Chemical Residues in Honey.Journal of the American Society for Mass Spectrometry · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Compound identification is a critical process in metabolomics. The widely used approach for compound identification in gas chromatography-mass spectrometry (GC-MS) based metabolomics is the spectrum matching, in which the mass spectral similarity between an experimental mass spectrum and each mass spectrum in a reference library is calculated. While various similarity measures have been developed to improve the overall accuracy of compound identification, little attention has been paid to reducing the false discovery rate. We, therefore, develop an approach for controlling false identification rate using the distribution of the difference between the first and the second highest spectral similarity scores. We further propose a model-based approach to achieving a desired true positive rate. The developed method is applied to the NIST mass spectral library and its performance is compared with the conventional approach that uses only the maximum spectral similarity score. The results show that the developed method achieves a significantly higher
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What OpenQuestion holds
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.