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@ARTICLE{Kalinin:301772,
      author       = {A. A. Kalinin and J. Arevalo and E. Serrano and L.
                      Vulliard$^*$ and H. Tsang and M. Bornholdt and A. F. Muñoz
                      and S. Sivagurunathan and B. Rajwa and A. E. Carpenter and
                      G. P. Way and S. Singh},
      title        = {{A} versatile information retrieval framework for
                      evaluating profile strength and similarity.},
      journal      = {Nature Communications},
      volume       = {16},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-01152},
      pages        = {5181},
      year         = {2025},
      abstract     = {Large-scale profiling assays capture a cell population's
                      state by measuring thousands of biological properties per
                      cell or sample. However, evaluating profile strength and
                      similarity remains challenging due to the high
                      dimensionality and non-linear, heterogeneous nature of
                      measurements. Here, we develop a statistical framework using
                      mean average precision (mAP) as a single, data-driven metric
                      to address this challenge. We validate the mAP framework
                      against established metrics through simulations and
                      real-world data, revealing its ability to capture subtle and
                      meaningful biological differences in cell state.
                      Specifically, we use mAP to assess a sample's phenotypic
                      activity relative to controls, as well as the phenotypic
                      consistency of groups of perturbations (or samples). We
                      evaluate the framework across diverse datasets and on
                      different profile types (image, protein, mRNA),
                      perturbations (CRISPR, gene overexpression, small
                      molecules), and resolutions (single-cell, bulk). The mAP
                      framework, together with our open-source software package
                      copairs, is useful for evaluating high-dimensional profiling
                      data in biological research and drug discovery.},
      keywords     = {Software / Humans / Gene Expression Profiling: methods /
                      Information Storage and Retrieval: methods / Computational
                      Biology: methods / Phenotype},
      cin          = {D260},
      ddc          = {500},
      cid          = {I:(DE-He78)D260-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40467541},
      doi          = {10.1038/s41467-025-60306-2},
      url          = {https://inrepo02.dkfz.de/record/301772},
}