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publications (hide)1637
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1Methods Used in the Development of Common Data Models for Health Data: Scoping Review
N. Ahmadi, M. Zoch, P. Kelbert, R. Noll, J. Schaaf, M. Wolfien, and M. Sedlmayr. JMIR medical informatics, (Aug 3, 2023)Publisher Copyright: © 2023 The British Editorial..
5 days ago by @scadsfct
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1Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration
W. Hahn, N. Ahmadi, K. Hoffmann, J. Eckardt, M. Sedlmayr, and M. Wolfien. Studies in health technology and informatics, (Aug 22, 2024)Publisher Copyright: © 2024 The Authors..
5 days ago by @scadsfct
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Acute,
Assessment,
Care
Electronic
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Health
Hematology,
Humans,
Information
Interoperability,
Leukemia,
Myeloid,
Outcome
Records,
topic_lifescienceAcute,Assessment,CareElectronicFIS_scadsHealthHematology,Humans,InformationInteroperability,Leukemia,Myeloid,OutcomeRecords,
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1Challenges and applications in generative AI for clinical tabular data in physiology
C. Umesh, M. Mahendra, S. Bej, O. Wolkenhauer, and M. Wolfien. Pflügers Archiv : European journal of physiology, (Oct 17, 2024)© 2024. The Author(s)..
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1Netrank: network-based approach for biomarker discovery
A. Al-Fatlawi, E. Rusadze, A. Shmelkin, N. Malekian, C. Ozen, C. Pilarsky, and M. Schroeder. BMC bioinformatics, (Jul 29, 2023)Publisher Copyright: © 2023, The Author(s)..
5 days ago by @scadsfct
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Algorithms,
Area
Biomedical
Curve,
Disease
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Gene
Humans,
Library
Progression,
Research,
Under
topic_lifescienceAlgorithms,AreaBiomedicalCurve,DiseaseFIS_scadsGeneHumans,LibraryProgression,Research,Under
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1Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance
N. Malekian, S. Sainath, A. Al-Fatlawi, and M. Schroeder. Frontiers in microbiology, (2023)Copyright © 2023 Malekian, Sainath, Al-Fatlawi and Schroeder..
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1Diatom adhesive trail proteins acquired by horizontal gene transfer from bacteria serve as primers for marine biofilm formation
J. Zackova Suchanova, G. Bilcke, B. Romanowska, A. Fatlawi, M. Pippel, A. Skeffington, M. Schroeder, W. Vyverman, K. Vandepoele, N. Kröger and 1 other author(s). New Phytologist, 240 (2): 770--783 (Aug 7, 2023)Publisher Copyright: © 2023 The Authors New Phytologist © 2023 New Phytologist Foundation. We acknowledge the support of the Core Facility Mass Spectrometry & Proteomics, Light Microscopy Facility and the Dresden‐concept Genome Center of the Technology Platform of the Center for Molecular and Cellular Bioengineering (CMCB) of the TU Dresden..
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Adhesives/metabolism,
Bacteria
Biofilms,
Diatoms/metabolism,
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Horizontal,
Transfer,
topic_lifescienceAdhesives/metabolism,BacteriaBiofilms,Diatoms/metabolism,FIS_scadsGeneHorizontal,Transfer,
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1Is Protein BLAST a thing of the past?
A. Al-Fatlawi, M. Menzel, and M. Schroeder. Nature communications, (Dec 11, 2023)
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Databases,
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Protein,
Proteins/genetics
topic_lifescienceDatabases,FIS_scadsProtein,Proteins/genetics
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1The Rad52 SSAP superfamily and new insight into homologous recombination
A. Al-Fatlawi, M. Schroeder, and {. Stewart. Communications biology, (Jan 23, 2023)Funding Information: We gratefully acknowledge support from ZIH of TU Dresden and the BMBF Project scads.ai..
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DNA
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Homologous
Protein/genetics
Rad52
Recombination
Recombination,
Repair
and
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1New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology
G. Breda, B. Derraz, C. Kaempf, F. Baenke, F. Cotte, K. Reiche, U. Köhl, {. Kather, D. Eskenazy, and {. Gilbert. npj Precision Oncology, (Jan 30, 2024)Publisher Copyright: © 2024, The Author(s)..
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1Spontaneous flow instabilities of active polar fluids in three dimensions
A. Singh, Q. Vagne, F. Jülicher, and {. Sbalzarini. Physical Review Research, (April 2023)Funding Information: Acknowledgments. This work was supported by the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, funded by the Federal Ministry of Education and Research (BMBF, Bundesministerium für Bildung und Forschung). We thank the Scientific Computing Facility of MPI-CBG and the Center for Information Services and High Performance Computing (ZIH) of TU Dresden for providing the compute resources for the numerical simulations. We thank Philipp Suhrcke (Sbalzarini group) and the anonymous referees for their constructive comments. Publisher Copyright: © 2023 authors. Published by the American Physical Society..
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1Inverse Dirichlet weighting enables reliable training of physics informed neural networks
S. Maddu, D. Sturm, {. Mueller, and {. Sbalzarini. Machine learning: science and technology, (Feb 15, 2022)Funding Information: This work was supported by the German Research Foundation (DFG) – EXC-2068, Cluster of Excellence ‘Physics of Life’, and by the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, funded by the Federal Ministry of Education and Research (BMBF). The present work was also partly funded by the Center for Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd..
5 days ago by @scadsfct
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ALGORITHM
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active
catastrophic
flow
forgetting,
gradient
modeling,
multi-objective
multi-scale
networks,
neural
physics-informed
regularization,
training,
turbulence,
topic_lifescienceALGORITHMFIS_scadsactivecatastrophicflowforgetting,gradientmodeling,multi-objectivemulti-scalenetworks,neuralphysics-informedregularization,training,turbulence,
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1STENCIL-NET for equation-free forecasting from data
S. Maddu, D. Sturm, {. Cheeseman, {. Müller, and {. Sbalzarini. Scientific reports, (December 2023)Publisher Copyright: © 2023, Springer Nature Limited..
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1A numerical solver for active hydrodynamics in three dimensions and its application to active turbulence
A. Singh, {. Suhrcke, P. Incardona, and {. Sbalzarini. Physics of fluids, (Oct 1, 2023)Publisher Copyright: © 2023 Author(s)..
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1A Unifying Mathematical Definition of Particle Methods
J. Pahlke, and {. Sbalzarini. IEEE open journal of the computer society : an open access publication of the IEEE Computer Society, (2023)Publisher Copyright: © 2020 IEEE..
5 days ago by @scadsfct
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Algorithmics,
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algorithms,
definition,
engineering
formal
mesh-free
methods,
parallelization,
particle
simulation
software
topic_lifescienceAlgorithmics,FIS_scadsalgorithms,definition,engineeringformalmesh-freemethods,parallelization,particlesimulationsoftware
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1Learning deterministic hydrodynamic equations from stochastic active particle dynamics
S. Maddu, Q. Vagne, and {. Sbalzarini. WorkingPaper, (Jan 21, 2022)
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1Parallel Discrete Convolutions on Adaptive Particle Representations of Images
J. Jonsson, {. Cheeseman, S. Maddu, K. Gonciarz, and {. Sbalzarini. IEEE Transactions on Image Processing, (Jan 1, 2022)Publisher Copyright: © 1992-2012 IEEE..
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Convolution,
Data
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Image
Microscopy,
Signal
processing,
reconstruction,
resolution
resolution,
structures,
topic_lifescienceConvolution,DataFIS_scadsImageMicroscopy,Signalprocessing,reconstruction,resolutionresolution,structures,
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1A C++ Library for Memory Layout and Performance Portability of Scientific Applications
P. Incardona, A. Gupta, S. Yaskovets, and {. Sbalzarini. Euro-Par 2022, page 109--120. Germany, Springer Science and Business Media B.V., (2023)Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 28th International European Conference on Parallel and Distributed Computing , Euro-Par 2022 ; Conference date: 22-08-2022 Through 26-08-2022.
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C++
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GPU,
algorithms,
generic
layout,
memory
multi-core,
performance
portability
tuples,
topic_lifescienceC++FIS_scadsGPU,algorithms,genericlayout,memorymulti-core,performanceportabilitytuples,
(0)
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1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
S. Perez, S. Maddu, {. Sbalzarini, and P. Poncet. Journal of computational physics, (Oct 15, 2023)Publisher Copyright: © 2023 The Author(s).
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Adaptive
Artificial
Bayesian
Carlo,
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Hamiltonian
Intelligence,
Monte
Multi-objective
Quantification
Uncertainty
learning,
networks,
neural
physics-informed
training,
weight
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1Stability selection enables robust learning of differential equations from limited noisy data
S. Maddu, {. Cheeseman, {. Sbalzarini, and {. Müller. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (June 2022)Publisher Copyright: © 2022 Royal Society Publishing. All rights reserved..
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PAR
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learning
learning,
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proteins,
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theory
topic_lifescienceFIS_scadsPARdifferentialequations,learninglearning,machineproteins,regression,selection,sparsestabilitystatisticaltheory
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1Error-Tolerant Reasoning in ℰℒ w.r.t. Optimal ABox Repairs (Extended Abstract)
F. Baader, F. Kriegel, and A. Nuradiansyah. Proceedings of the 36th International Workshop on Description Logics (DL 2023), Rhodes, Greece, September 2-4, 2023, CEUR-WS.org, (Sep 2, 2023)
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PUMA

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1Methods Used in the Development of Common Data Models for Health Data: Scoping Review

1Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration

1Challenges and applications in generative AI for clinical tabular data in physiology

1Netrank: network-based approach for biomarker discovery

1Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance

1Diatom adhesive trail proteins acquired by horizontal gene transfer from bacteria serve as primers for marine biofilm formation

1Is Protein BLAST a thing of the past?

1The Rad52 SSAP superfamily and new insight into homologous recombination

1New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology

1Spontaneous flow instabilities of active polar fluids in three dimensions

1Inverse Dirichlet weighting enables reliable training of physics informed neural networks

1STENCIL-NET for equation-free forecasting from data

1A numerical solver for active hydrodynamics in three dimensions and its application to active turbulence

1A Unifying Mathematical Definition of Particle Methods

1Learning deterministic hydrodynamic equations from stochastic active particle dynamics

1Parallel Discrete Convolutions on Adaptive Particle Representations of Images

1A C++ Library for Memory Layout and Performance Portability of Scientific Applications

1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

1Stability selection enables robust learning of differential equations from limited noisy data

1Error-Tolerant Reasoning in ℰℒ w.r.t. Optimal ABox Repairs (Extended Abstract)

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