ArticleMethods in molecular biology (Clifton, N.J.)2023
Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis.
Article in Methods in molecular biology (Clifton, N.J.), 2023. 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
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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.
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, 1 citations in OpenAlex.
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this chapter, we review the cutting-edge statistical and machine learning methods for missing value imputation, normalization, and downstream analyses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven methods, internal standard-based, and quality control sample-based normalization. We also summarize different types of statistical analysis for metabolomics and clinical outcomes, such as inference on metabolic biomarkers, clustering of metabolomic profiles, metabolite module building, and integrative analysis with transcriptome.
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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.