Cross-platform metabolomics imputation using importance-weighted autoencoders.

Alexander Smith, Rui Pinto, Loukas Zagkos, Ioanna Tzoulaki, Paul Elliott, Abbas Dehghan

Abstract read

In one paragraph

Article in NPJ systems biology and applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

cpiVAE: Robust and Interpretable Cross-Platform Proteomics Imputation.bioRxiv : the preprint server for biology · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alexander SmithDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. alexander.smith@imperial.ac.uk.

Rui PintoDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Loukas ZagkosDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Paul ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Abbas DehghanDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Funding

Wellcome TrustWellcome Trust 206046/Z/17/Z

6 · The paper itself

Abstract

Metabolomics data are often generated through different platforms and quantification methods which makes their synthesis and large-scale replication challenging. This study developed an ensemble of importance-weighted autoencoders to perform cross-platform metabolomics imputation between two metabolomics platforms, Metabolon and National Phenome Centre (NPC) at Imperial College, using 979 samples from the Airwave Health Monitoring Study. The generated samples were highly correlated with real values across all metabolites (µ

Indexed as

MetabolomicsAutoencoderC-Reactive ProteinHumansMetabolomeC-Reactive Protein

Identifiers

PMID41513683

DOI10.1038/s41540-025-00644-5

PMCPMC12894875

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LicenceCC BY

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Registered trials

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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.