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@ARTICLE{Sharma:298556,
      author       = {S. Sharma and R. Sauter and M. Hotze and A. M. P. Prowatke
                      and M. Niere and T. Kipura and A.-S. Egger and K.
                      Thedieck$^*$ and M. Kwiatkowski and M. Ziegler and I.
                      Heiland},
      title        = {{GEMCAT}-a new algorithm for gene expression-based
                      prediction of metabolic alterations.},
      journal      = {NAR: genomics and bioinformatics},
      volume       = {7},
      number       = {1},
      issn         = {2631-9268},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-00281},
      pages        = {lqaf003},
      year         = {2025},
      abstract     = {The interpretation of multi-omics datasets obtained from
                      high-throughput approaches is important to understand
                      disease-related physiological changes and to predict
                      biomarkers in body fluids. We present a new
                      metabolite-centred genome-scale metabolic modelling
                      algorithm, the Gene Expression-based Metabolite Centrality
                      Analysis Tool (GEMCAT). GEMCAT enables integration of
                      transcriptomics or proteomics data to predict changes in
                      metabolite concentrations, which can be verified by targeted
                      metabolomics. In addition, GEMCAT allows to trace measured
                      and predicted metabolic changes back to the underlying
                      alterations in gene expression or proteomics and thus
                      enables functional interpretation and integration of
                      multi-omics data. We demonstrate the predictive capacity of
                      GEMCAT on three datasets and genome-scale metabolic networks
                      from two different organisms: (i) we integrated
                      transcriptomics and metabolomics data from an engineered
                      human cell line with a functional deletion of the
                      mitochondrial NAD transporter; (ii) we used a large
                      multi-tissue multi-omics dataset from rats for
                      transcriptome- and proteome-based prediction and
                      verification of training-induced metabolic changes and
                      achieved an average prediction accuracy of $70\%;$ and (iii)
                      we used proteomics measurements from patients with
                      inflammatory bowel disease and verified the predicted
                      changes using metabolomics data from the same patients. For
                      this dataset, the prediction accuracy achieved by GEMCAT was
                      $79\%.$},
      keywords     = {Humans / Animals / Algorithms / Rats / Metabolomics:
                      methods / Proteomics: methods / Inflammatory Bowel Diseases:
                      genetics / Inflammatory Bowel Diseases: metabolism /
                      Transcriptome / Gene Expression Profiling: methods / Cell
                      Line},
      cin          = {ED01},
      ddc          = {570},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39897103},
      pmc          = {pmc:PMC11783570},
      doi          = {10.1093/nargab/lqaf003},
      url          = {https://inrepo02.dkfz.de/record/298556},
}