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@ARTICLE{Ing:303199,
      author       = {A. Ing and A. Andrades and M. R. Cosenza and J. Korbel$^*$},
      title        = {{I}ntegrating multimodal cancer data using deep latent
                      variable path modelling.},
      journal      = {Nature machine intelligence},
      volume       = {7},
      number       = {7},
      issn         = {2522-5839},
      address      = {[London]},
      publisher    = {Springer Nature Publishing},
      reportid     = {DKFZ-2025-01550},
      pages        = {1053 - 1075},
      year         = {2025},
      note         = {#LA:B480#},
      abstract     = {Cancers are commonly characterized by a complex pathology
                      encompassing genetic, microscopic and macroscopic features,
                      which can be probed individually using imaging and omics
                      technologies. Integrating these data to obtain a full
                      understanding of pathology remains challenging. We introduce
                      a method called deep latent variable path modelling, which
                      combines the representational power of deep learning with
                      the capacity of path modelling to identify relationships
                      between interacting elements in a complex system. To
                      evaluate the capabilities of deep latent variable path
                      modelling, we initially trained a model to map dependencies
                      between single-nucleotide variant, methylation profiles,
                      microRNA sequencing, RNA sequencing and histological data
                      using breast cancer data from The Cancer Genome Atlas. This
                      method exhibited superior performance in mapping
                      associations between data types compared with classical path
                      modelling. We additionally performed successful applications
                      of the model to stratify single-cell data, identify
                      synthetic lethal interactions using CRISPR-Cas9 screens
                      derived from cell lines and detect
                      histologic-transcriptional associations using spatial
                      transcriptomic data. Results from each of these data types
                      can then be understood with reference to the same holistic
                      model of illness.},
      keywords     = {Breast cancer (Other) / Computer science (Other) / Data
                      integration (Other) / Machine learning (Other)},
      cin          = {B480},
      ddc          = {004},
      cid          = {I:(DE-He78)B480-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40709098},
      pmc          = {pmc:PMC12283373},
      doi          = {10.1038/s42256-025-01052-4},
      url          = {https://inrepo02.dkfz.de/record/303199},
}