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@PHDTHESIS{Lagemann:269684,
      author       = {Lagemann, Kai},
      title        = {{D}eep {L}earning for {C}ausal {I}nference and {L}atent
                      {D}ynamical {M}odeling in {B}iomedical {R}esearch},
      school       = {Rheinische Friedrich-Wilhelms-Universität Bonn},
      type         = {Dissertation},
      reportid     = {DZNE-2024-00598},
      pages        = {207p},
      year         = {2024},
      note         = {Dissertation, Rheinische Friedrich-Wilhelms-Universität
                      Bonn, 2024},
      abstract     = {Biological systems are ubiquitous, encompassing complex
                      molecular networks governing single-cell organisms to
                      expansive ecosystems profoundly impacting our planet's
                      environment. In biology, the adoption of a systems approach
                      seeks to achieve a comprehensive, quantitative understanding
                      of living organisms comparable in some ways to the kind of
                      understanding we have of systems in engineering and physics.
                      In this context, a major challenge in scientific AI is
                      causal learning. To address emerging biomedical questions,
                      this work proposes a deep neural architecture that learns
                      causal relationships between variables by combining
                      high-dimensional data with prior causal knowledge. In
                      particular a combination of convolutional and graph neural
                      networks is utilized within a causal risk framework,
                      specifically designed to handle the high dimensionality and
                      typical sources of noise frequently occurring in large-scale
                      biological data. In experimental evaluations, the proposed
                      learner demonstrate its effectiveness in identifying novel
                      causal relationships among thousands of variables. The
                      results are based on extensive gold-standard simulations
                      with known ground-truth. Additionally, real biological
                      examples are considered, where the models are applied to
                      high-dimensional molecular data and their output compared
                      against entirely unseen validation experiments. These
                      findings showcase the feasibility of using deep neural
                      approaches to learn causal networks at a large
                      scale.Additionally, this work presents a novel method for
                      learning dynamical systems from high-dimensional empirical
                      data combining variational autoencoders and spatio-temporal
                      attention within a framework that enforces
                      scientifically-motivated invariances. The focus is set to
                      scenarios in which data are available from multiple
                      different instances of a system whose underlying dynamical
                      model is entirely unknown at the outset. The presented
                      approach builds upon a separation, dividing the encoding
                      into instance-specific information and a universal latent
                      dynamics model shared across all instances. This separation
                      is achieved automatically and driven solely by empirical
                      data. The results offer a promising new framework for
                      efficiently learning dynamical models from heterogeneous
                      data. This framework has the potential for applications in
                      various fields, including physics, medicine, biology, and
                      engineering.In a different approach, this work explores
                      interventional experiments to shed light on the causal
                      structure within a system. Under the framework of
                      instrumental variables, a new and mathematically sound
                      cause-effect estimator is proposed to uncover sparse causal
                      relations based on unpaired data regimes. The primary focus
                      lies in predicting the outcomes of interventions that have
                      not been performed before, based on data gathered from
                      observed interventions with unknown characteristics. To
                      illustrate, this framework addresses inquiries such as how
                      hypothetical alterations through gene-level interventions
                      could impact the growth rate of a cell. The efficacy of this
                      method is studied on simulated benchmarks and semi-simulated
                      test cases incorporating human single cell
                      measurements.Last, this work intends to advance the
                      prediction and comprehension of individual treatment effects
                      in a longitudinal setting. Specifically, this work is
                      investigating clinical records of patients afflicted with
                      wet age-related macular degeneration which if untreated can
                      lead to severe vision loss and legal blindness. To gain a
                      comprehensive understanding of this disease progression,
                      supervised end-to-end models are devised and evaluated to
                      estimate drug responses based on highly irregular
                      time-series data and forecast future treatment effects at
                      individual patient level.},
      cin          = {AG Mukherjee},
      cid          = {I:(DE-2719)1013030},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:5-76266},
      url          = {https://pub.dzne.de/record/269684},
}