001052327 001__ 1052327
001052327 005__ 20260220104604.0
001052327 037__ $$aFZJ-2026-00935
001052327 1001_ $$0P:(DE-Juel1)192120$$aBazarova, Alina$$b0$$eCorresponding author
001052327 1112_ $$aISCB-AFRICA ASBCB 2025$$cCapetown$$d2025-04-17 - 2025-04-17$$wSouth Africa
001052327 245__ $$aSimulation-Based Inference for Computational Biology: Integrating AI, Bayesian Modeling, and HPC
001052327 260__ $$c2025
001052327 3367_ $$2DRIVER$$alecture
001052327 3367_ $$031$$2EndNote$$aGeneric
001052327 3367_ $$2BibTeX$$aMISC
001052327 3367_ $$0PUB:(DE-HGF)17$$2PUB:(DE-HGF)$$aLecture$$blecture$$mlecture$$s1769505449_7752$$xOther
001052327 3367_ $$2ORCID$$aLECTURE_SPEECH
001052327 3367_ $$2DataCite$$aText
001052327 520__ $$aThis 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.
001052327 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001052327 536__ $$0G:(DE-Juel-1)E54.303.11$$aHelmholtz AI Consultant Team FB Information (E54.303.11)$$cE54.303.11$$x1
001052327 7001_ $$0P:(DE-Juel1)195622$$aRobledo, Jose Ignacio$$b1$$eCorresponding author
001052327 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b2$$eCorresponding author
001052327 909CO $$ooai:juser.fz-juelich.de:1052327$$pVDB
001052327 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192120$$aForschungszentrum Jülich$$b0$$kFZJ
001052327 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)195622$$aForschungszentrum Jülich$$b1$$kFZJ
001052327 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185654$$aForschungszentrum Jülich$$b2$$kFZJ
001052327 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001052327 9141_ $$y2025
001052327 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001052327 980__ $$alecture
001052327 980__ $$aVDB
001052327 980__ $$aI:(DE-Juel1)JSC-20090406
001052327 980__ $$aUNRESTRICTED