ArticleNucleic acids research2020
NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- Insights into the Serum Metabolic Adaptations in Response to Inspiratory Muscle Training: A Metabolomic Approach Based onInternational journal of molecular sciences · 2023Trial
- Serum untargeted metabolomics reveals key pathways in feline mammary carcinoma for comparative oncology.Metabolomics : Official journal of the Metabolomic Society · 2026Article
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- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification.Computational and structural biotechnology journal · 2026Article
- Deciphering cross-cohort metabolic signatures of immune responses and their implications for disease pathogenesis.Molecular systems biology · 2025Article
- Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- The mitochondrial dicarboxylate carrier mediates in vivo hepatic gluconeogenesis.bioRxiv : the preprint server for biology · 2024Article
- Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.Computers in biology and medicine · 2024Article
- Antiprotozoal peptide prediction using machine learning with effective feature selection techniques.Heliyon · 2024Article
- FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.Briefings in bioinformatics · 2024Article
- Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction.Scientific reports · 2024Article
- VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.Nucleic acids research · 2024Article
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- TheMarker: a comprehensive database of therapeutic biomarkers.Nucleic acids research · 2024Article
- TTD: Therapeutic Target Database describing target druggability information.Nucleic acids research · 2024Article
- Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0.GigaScience · 2024Article
- Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.Journal of the American Society for Mass Spectrometry · 2023Article
- Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data.Bioinformatics (Oxford, England) · 2023Article
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9 authors.
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Abstract
Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.
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