ArticleCommunications biology2022
Variational autoencoders learn transferrable representations of metabolomics data.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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.
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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
27 citing papers in PubMed.
- Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach.Health science reports · 2026Article
- Eigenlipids for exploring lipid biology.Journal of lipid research · 2026Review
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- Metabolomic analysis for diagnosis of oral squamous cell carcinoma using machine learning.Discover oncology · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Statistical modelling of an outcome variable with integrated multi-omics.BMC bioinformatics · 2025Article
- Emerging Technologies for Exploring the Cellular Mechanisms in Vascular Diseases.International journal of molecular sciences · 2025Review
- mzLearn as a data-driven LC/MS signal detection algorithm that enables pre-trained generative models for untargeted metabolomics.Communications chemistry · 2025Article
- Model Predictive Control on the Neural Manifold.Neural computation · 2025Article
- Cytoplasmic dynamics are overlooked in single nuclei RNA-seq but can be rescued by CytoRescue, a generative AI model to recover cytoplasm enriched gene.bioRxiv : the preprint server for biology · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.PLoS computational biology · 2025Article
- AI-enabled manufacturing process discovery.PNAS nexus · 2025Article
- Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement.Frontiers in plant science · 2025Article
- From data to decisions: a paradigm shift in fruit agriculture through the integration of multi-omics, modern phenotyping, and cutting-edge bioinformatic tools.Frontiers in plant science · 2025Review
- AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium.International journal of molecular sciences · 2024Article
- A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes.Nature communications · 2024Article
- Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.Nature genetics · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.
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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 Socratic graph.