Evidence map›Paper›PMID 42230996›Full record

ArticleCommunications biology2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

Ryan Schildcrout, Kirk Smith, Rupa Bhowmick, Yuntao Lu, Suraj Menon, Minali Kapadia, Emily Kurtz, Anya Coffeen-Vandeven, Srikar Nelakuditi, Sriram Chandrasekaran

Abstract read
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In one paragraph

Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Ryan SchildcroutDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0009-0008-9502-1504
Kirk SmithDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Rupa BhowmickDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Yuntao LuDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Suraj MenonDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Minali KapadiaDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Emily KurtzDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
Anya Coffeen-VandevenDepartment of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
Srikar NelakuditiDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Sriram ChandrasekaranDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. csriram@umich.edu.ORCID http://orcid.org/0000-0002-8405-5708

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Indexed as

Machine LearningMetabolic Networks and PathwaysMetabolomeMetabolomicsCell Line, TumorHumansMultiomicsProteomicsTranscriptome

Identifiers

PMID42230996
PMCPMC13534617

What OpenQuestion holds

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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.