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      Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

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      Embargo Lift Date: 2022-06-30
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      Author(s)
      Babur, Ö.
      Luna, A.
      Korkut, A.
      Durupınar, F.
      Siper, M. C.
      Doğrusöz, Uğur
      Jacome, A. S. V.
      Peckner, R.
      Christiansen, K. E.
      Jaffe, J.D
      Spellman, P.T.
      Aslan, J. E.
      Sander, C.
      Demir, E.
      Date
      2021-06
      Source Title
      Patterns
      Print ISSN
      2666-3899
      Publisher
      Cell Press
      Volume
      2
      Issue
      6
      Pages
      100257-1 - 100257-12
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.
      Keywords
      Proteomics
      Causal pathway analysis
      Cancer
      Permalink
      http://hdl.handle.net/11693/77469
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.patter.2021.100257
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      • Department of Computer Engineering 1561
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