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@MISC{Bazarova:1052327,
      author       = {Bazarova, Alina and Robledo, Jose Ignacio and Kesselheim,
                      Stefan},
      title        = {{S}imulation-{B}ased {I}nference for {C}omputational
                      {B}iology: {I}ntegrating {AI}, {B}ayesian {M}odeling, and
                      {HPC}},
      reportid     = {FZJ-2026-00935},
      year         = {2025},
      abstract     = {This tutorial introduces Simulation-Based Inference (SBI),
                      a framework combining Bayesian modeling, AI techniques, and
                      high-performance computing (HPC) to address key challenges
                      in computational biology, such as performing reliable
                      inference with limited data by using AI-based approximate
                      Bayesian computation. Moreover, it tackles the problem of
                      intractable likelihood functions, thereby allowing to
                      utilize Bayesian inference for biological systems with
                      multiple sources of stochasticity. The tutorial also
                      demonstrates how to leverage HPC environments to drastically
                      reduce inference runtimes, making it highly relevant for
                      large-scale biological problems. This tutorial bridges
                      theoretical foundations with hands-on applications in
                      computational biology. Participants will learn to implement
                      SBI frameworks using diverse biological models, such as
                      molecular dynamics simulations, agent-based tumor growth
                      models, count data modeling, and Lotka-Volterra systems.
                      Practical exercises in Jupyter notebooks guide attendees
                      through SBI workflows, from simple coin-flipping examples to
                      more complex biological simulations, ensuring accessibility
                      for participants with varied backgrounds. The tutorial’s
                      inclusion of cutting-edge methods like Sequential Neural
                      Posterior Estimation and its emphasis on parallelization and
                      HPC scalability align closely with the scientific
                      community's focus on innovation in computational biology. A
                      previous iteration of the tutorial at the Helmholtz AI
                      Conference 2024 received excellent reviews and led to
                      interdisciplinary discussions, highlighting its broad
                      applicability and impact. For this conference, the content
                      has been further refined with additional examples relevant
                      to the community, ensuring it meets the needs of
                      bioinformatics researchers.},
      month         = {Apr},
      date          = {2025-04-17},
      organization  = {ISCB-AFRICA ASBCB 2025, Capetown
                       (South Africa), 17 Apr 2025 - 17 Apr
                       2025},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / Helmholtz AI Consultant
                      Team FB Information (E54.303.11)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)E54.303.11},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/1052327},
}