% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Paczkowska:177480,
      author       = {M. Paczkowska and J. Barenboim and N. Sintupisut and N. S.
                      Fox and H. Zhu and D. Abd-Rabbo and M. W. Mee and P. C.
                      Boutros and P. Drivers and
                      FunctionalInterpretationWorkingGroup and J. Reimand and
                      PCAWGConsortium},
      title        = {{I}ntegrative pathway enrichment analysis of multivariate
                      omics data.},
      journal      = {Nature Communications},
      volume       = {11},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2021-02567},
      pages        = {735},
      year         = {2020},
      note         = {siehe Correction: DKFZ Autoren affiliiert im PCAWG
                      Consortium: https://inrepo02.dkfz.de/record/212438 /
                      https://doi.org/10.1038/s41467-022-32342-9},
      abstract     = {Multi-omics datasets represent distinct aspects of the
                      central dogma of molecular biology. Such high-dimensional
                      molecular profiles pose challenges to data interpretation
                      and hypothesis generation. ActivePathways is an integrative
                      method that discovers significantly enriched pathways across
                      multiple datasets using statistical data fusion,
                      rationalizes contributing evidence and highlights associated
                      genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole
                      Genomes (PCAWG) Consortium, which aggregated whole genome
                      sequencing data from 2658 cancers across 38 tumor types, we
                      integrated genes with coding and non-coding mutations and
                      revealed frequently mutated pathways and additional cancer
                      genes with infrequent mutations. We also analyzed prognostic
                      molecular pathways by integrating genomic and transcriptomic
                      features of 1780 breast cancers and highlighted associations
                      with immune response and anti-apoptotic signaling.
                      Integration of ChIP-seq and RNA-seq data for master
                      regulators of the Hippo pathway across normal human tissues
                      identified processes of tissue regeneration and stem cell
                      regulation. ActivePathways is a versatile method that
                      improves systems-level understanding of cellular
                      organization in health and disease through integration of
                      multiple molecular datasets and pathway annotations.},
      keywords     = {Adenocarcinoma: genetics / Adenocarcinoma: metabolism /
                      Apoptosis: genetics / Breast Neoplasms: genetics / Breast
                      Neoplasms: immunology / Breast Neoplasms: metabolism /
                      Breast Neoplasms: mortality / Chromatin Immunoprecipitation
                      / Computational Biology: methods / Databases, Factual /
                      Female / Gene Dosage / Gene Expression Profiling / Gene
                      Regulatory Networks / Genomics: methods / Humans / Metabolic
                      Networks and Pathways: genetics / Mutation / Neoplasms:
                      genetics / Neoplasms: metabolism / Prognosis /
                      Protein-Serine-Threonine Kinases: genetics /
                      Protein-Serine-Threonine Kinases: metabolism / RNA,
                      Messenger: genetics / Sequence Analysis, RNA / Signal
                      Transduction / RNA, Messenger (NLM Chemicals) / Hippo
                      protein, human (NLM Chemicals) / Protein-Serine-Threonine
                      Kinases (NLM Chemicals)},
      cin          = {B080 / B240 / B370 / B330 / HD01 / B060 / B360 / BE01 /
                      B062 / B066 / B063 / W190 / B260 / W610 / B087},
      ddc          = {500},
      cid          = {I:(DE-He78)B080-20160331 / I:(DE-He78)B240-20160331 /
                      I:(DE-He78)B370-20160331 / I:(DE-He78)B330-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)B060-20160331 /
                      I:(DE-He78)B360-20160331 / I:(DE-He78)BE01-20160331 /
                      I:(DE-He78)B062-20160331 / I:(DE-He78)B066-20160331 /
                      I:(DE-He78)B063-20160331 / I:(DE-He78)W190-20160331 /
                      I:(DE-He78)B260-20160331 / I:(DE-He78)W610-20160331 /
                      I:(DE-He78)B087-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32024846},
      pmc          = {pmc:PMC7002665},
      doi          = {10.1038/s41467-019-13983-9},
      url          = {https://inrepo02.dkfz.de/record/177480},
}