ArticleJournal of the American Society for Mass Spectrometry2023
Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.
Article in Journal of the American Society for Mass Spectrometry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The FINESSE (Artificial Intelligence Stress Echo) study to develop and validate machine learning-based model to improve risk prediction in patients undergoing stress echocardiography for the assessment of inducible myocardial ischaemia (FINESSE Protocol).European heart journal. Imaging methods and practice · 2026Article
- Feature Down-Selection to Improve Supervised Classification by Machine Learning on Mass Spectrometry Imaging Data.Molecules (Basel, Switzerland) · 2026Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Multiplexed Quantification of First-Trimester Serum Biomarkers in Healthy Pregnancy.International journal of molecular sciences · 2025Article
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Exploring Sample Storage Conditions for the Mass Spectrometric Analysis of Extracted Lipids from Latent Fingerprints.Biomolecules · 2025Article
- Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex.Molecules (Basel, Switzerland) · 2025Article
- Skin Surface Sebum Analysis by ESI-MS.Biomolecules · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a "DIY" aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.
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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.