News & Publications

Press Coverage

Presentations

Date	Title
2018-06-28	Helmholtz Analytics Framework (PDF, 2 MB) , Vancouver
2017-06-12	Project Proposal Presentation, Berlin (PDF, 642 kB)

Events

Publications

2018

• R. Gutzen, M. von Papen, G. Trensch, P. Quaglio, S. Grün and M. Denker (2018) Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Front. Neuroinform. 12:90. doi:10.3389/fninf.2018.00090 https://www.frontiersin.org/articles/10.3389/fninf.2018.00090/abstract
• J. Schroeter, M. Braun, R. Ruhnke, P. Braesicke (2018) Interactive versus prescribed ozon in the ICON-ART climate model: how is the spectrum of variability changing? Poster A41I-3081 presented at 2018 Fall Meeting, AGU, Washington, D.C., 10-14 Dec.,  Interactive versus prescribed ozone in the ICON-ART climate model: how is the spectrum of variability changing? (PDF, 5 MB)
• M. Götz and H. Anzt, Machine Learning-Aided Numerical Linear Algebra: Convolutional Neural Networks for the Efficient Preconditioner Generation, in 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA), 2018, pp. 49–56.
• K. Krajsek, C. Comito, M. Götz, B. Hagemeier, Ph. Knechtges, M. Siggel, The Helmholtz Analytics Toolkit (HeAT) - A Scientific Big Data Library for HPC, Extreme Data – Demands, Technologies, and Services, Jülich, 18.-19. Sept. 2018, IAS Series Vol. 40, 2019, 57-60: http://hdl.handle.net/2128/22029.

2019

• M. Weiel, I. Reinartz, A. Schug (2019) Rapid interpretation of small-angle X-ray scattering data. PLOS Computational Biology 15(3): e1006900. https://doi.org/10.1371/journal.pcbi.1006900
• Karunakar R. Pothula, Daryna Smyrnova , Gunnar F. Schröder, Clustering cryo-EM images of helical protein polymers for helical reconstructions, Ultramicroscopy (2019) 203:132-138, https://doi.org/10.1016/j.ultramic.2018.12.009.
• M. Bockwoldt, D. Houry, M. Niere, T.I. Gossmann, I. Reinartz, A. Schug, M. Ziegler, and I. Heiland. Identifying evolutionary and kinetic drivers of NAD-dependent signaling. Proc. Natl. Acad. Sci. U.S.A., 116 (32), 15957-15966, 2019.
• O. Taubert, I. Reinartz, H. Meyerhenke, and A. Schug. diSTruct v1.0: Generating Biomolecular Structures from Distance Constraints. Bioinformatics, Volume 35, Issue 24, 15 December 2019, Pages 5337–5338, https://doi.org/10.1093/bioinformatics/btz578.
• D. Todt. Untersuchung räumlicher Muster in meteorologischen Modellen für die Ensemble-Kalibrierung mittels tiefer neuronaler Netze [Engl.: "Examination of Spatial Patterns in Meteorological Models for Ensemble Calibration Using Neural Networks"]. Bachelor thesis, 2019. http://hdl.handle.net/2128/24026.
• Julian Moosmann, Florian Wieland, Berit Zeller-Plumhoff, Silvia Galli, Diana Krüger, Alexey Ershov, Silke Lautner, Julian Sartori, Mason Dean, Sebastian Köhring, Hilmar Burmester, Thomas Dose, Niccoló Peruzzi, Ann Wennerberg, Regine Willumeit-Römer, Fabian Wilde, Philipp Heuser, Jörg U. Hammel, Felix Beckmann (2019) A load frame for in situ tomography at PETRA III. Proceedings Volume 11113, Developments in X-Ray Tomography XII; 1111318 (2019) https://doi.org/10.1117/12.2530445 Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States
• Benjamin Bourgart. Massiv parallele Datenanalyse für die Erdsystemmodellierung mit dem Helmholtz Analytics Toolkit [Engl: "Massively Parallel Data Analysis for Earth System Modelling Using the Helmholtz Analytics Toolkit"]. Bachelor thesis, FH Aachen, 2019.
• Braun, M., Schröter, J., Ruhnke, R., & Braesicke, P. (2019). Ozone hole induced southern hemispheric climate change signals in ICON-ART climate simulations. Presented at EGU General Assembly 2019, Vienna, 7-12 April.
• Görtz, Stefan (2019) Selbstlernende aerodynamische Modelle. Eingeladener Vortrag, Künstliche Intelligenz in der Luft- und Raumfahrt, 13. Nov. 2019, Technische Hochschule Wildau und Zentrum für Luft- und Raumfahrt Schönefelder Kreuz, Wildau, Germany.
• Görtz, Stefan (2019) Selbstlernende aerodynamische Modelle. Workshop Künstliche Intelligenz in der Luft- und Raumfahrt, DGLR, Fachausschuss Q3.4 Softwareengineering, 09. Okt. 2019, Garching, Germany.
• Hoffmann, Nils (2019) Entwicklung eines mit Hilfe von CFD Simulationen trainierten Autoencoders zwecks schneller Vorhersage aerodynamischer Daten für Flügelprofile, Bachelorarbeit, September 2019
• Kinting S, Li Y, Forstner M, Delhommel F, Sattler M, Griese M Potentiation of ABCA3 lipid transport function by ivacaftor and genistein. (2019) J Cell Mol Med 23, 5225-5234. doi: 10.1111/jcmm.14397.
• Kalel, V. C., Li, M., Gaussmann, S., Delhommel, F., Schäfer, A. B., Tippler, B., Jung, M., Maier, R., Oeljeklaus, S., Schliebs, W., Warscheid, B., Sattler, M., & Erdmann, R. (2019). Evolutionary divergent PEX3 is essential for glycosome biogenesis and survival of trypanosomatid parasites. Biochimica et Biophysica Acta - Molecular Cell Research. doi: 10.1016/j.bbamcr.2019.07.015.
• Goergen, K.; Bastin, S.; Bourgart, B.; Cardoso, R. M.; Coquelin, D.; Martynov, A.; Fernandez, J.; Giannaros, T. M.; Hodnebrog, Kartsios, S.; Katragkou, E.; Lorenz, T.; Milovac, J.; Warrach-Sagi, K.; Soares, P.; Sobolowski, S.; Truhetz, H. & Kollet, S. (2019). Soil moisture-temperature coupling in a CORDEX FPS convection-permitting WRF RCM ensemble. ICRC-CORDEX 2019 International Conference on Regional Climate, October 14-18, Beijing, China
• Goergen, K.; Bastin, S.; Bourgart, B.; Cardoso, R. M.; Coquelin, D.; Martynov, A.; Fernandez, J.; Giannaros, T. M.; Hodnebrog, Kartsios, S.; Katragkou, E.; Lorenz, T.; Milovac, J.; Warrach-Sagi, K.; Soares, P.; Sobolowski, S.; Truhetz, H. & Kollet, S. (2019). Soil moisture-temperature coupling in a CORDEX FPS convection-permitting WRF RCM ensemble. Latsis Symposium 2019 High-Resolution Climate Modeling: Perspectives and Challanges, August 21-23, Zürich, Switzerland
• Cavallaro, Gabriele, et al. "Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems." Proceedings of the Conference on Big Data from Space (BiDS'19). doi: 0.2760/848593, 2019.
• Krajsek, K.; Comito, C.; Coquelin, D.; Hagemeier, B.; Götz, M.; Hanselmann, S.; Debus, C.; Knechtges, P.; Schmitz, S. & Siggel, M. Das Helmholtz Analytics Toolkit (HeAT) - Eine High Performance Computing (HPC) Programmbibliothek für wissenschaftliche Big Data Analytik / The Helmholtz Analytics Toolkit (HeAT) - A High Performance Computing (HPC) Library for Scientific Big Data Analytics, Bernstein Koordinationsstelle (BCOS), 2019, -, 32-33

2020

• Görtz, Stefan (2020) Reduced-Order Modeling for Aerodynamic Applications and MDO, VKI Lecture Series on Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures, Brussels, Friday 28 February 2020.
• Barthelmes K, Ramcke E, Kang, H-S, Sattler M, Itzen A (2020) Conformational control of small GTPases by AMPylation. PNAS, in press.
• H.S. Kang, C. Sánchez-Rico, S. Ebersberger, F. X. Reymond Sutandy, A. Busch, T. Welte, R. Stehle, C. Hipp, L. Schulz, A. Buchbender, K. Zarnack, J. König, M. Sattler, An autoinhibitory intramolecular interaction proof-reads RNA recognition by the essential splicing factor U2AF2. Proc. Natl. Acad. Sci. U.S.A. 117 (13) 7140-7149; doi:10.1073/pnas.1913483117, 2020 .
• Dorit Jerger, Daniel Todt, Jonas Berndt, Hendrik Elbern, Kai Krajsek (2020) Cloud and Solar Power Prediction within the Helmholtz Analytics Framework. Poster at the 4th workshop on assimilating satellite cloud and precipitation observations for NWP, Reading, UK. Download.
• Yegenoglu A., Krajsek K., Pier S.D., Herty M. (2020) Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent. In: Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_7
• Götz, M., D., Debus, Coquelin, C., Krajsek, K., Comito, C., Knechtges, P., .Hagemeier, B., Tarnawa, M., Hanselmann, S., Siggel, S., Basermann, A. & Streit, A. (2020). HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. In Proceedings of the 19th IEEE International Conference on Big Data (BigData) (pp. 276-288). IEEE.
• O. Taubert, M. Götz, A. Schug and A. Streit, "Loss Scheduling for Class-Imbalanced Image Segmentation Problems," 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020, pp. 426-431, doi: 10.1109/ICMLA51294.2020.00073.
• J. Ohm, "Skalierbare Klassifikation von Remote Sensing Daten [engl.: Scalable Classification of Remote Sensing Data]", Bachelor thesis, Karlsruhe Institute of Technology (KIT), 2020.
  
• Zaucha, J.; Softley, C. A.; Sattler, M.; Frishman, D.; Popowicz, G. M. Deep Learning Model Predicts Water Interaction Sites on the Surface of Proteins Using Limited-Resolution Data. Chem. Commun.56 (98), 15454–15457. https://doi.org/10.1039/D0CC04383D, 2020.
• Voronin, M. Weiel, and A. Schug. Including residual contact information into replica-exchange MD simulations significantly enriches native-like
  conformations. PloS One 15 (11), e0242072, 2020.
• Delhommel, F. & Sattler, M. When Less Is More: Combining Site-Specific Isotope Labeling and NMR Unravels Structural Details of Huntingtin Repeats. Structure 28, 730–732, 2020.
• Delhommel F, Gabel F, Sattler M, Current approaches for integrating solution NMR spectroscopy and small-angle scattering to study the structure and dynamics of biomolecular complexes. J Mol Biol. doi:10.1016/j.jmb.2020.03.014, 2020.
• Fino R, Byrne R, Softley CA, et al, Introducing the CSP Analyzer: a Novel Machine Learning-based Application for Automated Analysis of two-dimensional NMR spectra in NMR Fragment-based Screening. Comput Struct Biotechnol J 18:603–611. doi: 10.1016/j.csbj.2020.02.015, 2020.
• I. Reinartz, M Weiel, and A. Schug. Combination and Interplay of FRET and SAXS for Protein Structure Analysis. Isr. J. Chem. 60 (7), 725-734, 2020.
• C. Röder, T. Kupreichyk, L. Gremer, L.U. Schäfer, K.R. Pothula, R.B.G. Ravelli, D. Willbold, W. Hoyer and G.F. Schröder. Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with Aβ fibrils. Nat. Struct. Mol. Biol. 27:660–667, 2020.
• James A. Geraets, Karunakar R. Pothula and Gunnar F. Schröder Integrating cryo-EM and NMR data. Curr. Opin. Struct. Biol. 61:173–181, 2020.

2021

• Schuch, L. A. et al. FARS1-related disorders caused by bi-allelic mutations in cytosolic phenylalanyl-tRNA synthetase genes: Look beyond the lungs! Clinical Genetics, doi:10.1111/cge.13943, 2021.
• Karunakar, J.A. Pothula, I. Geraets, I. Ferber, and G.F. Schröder. Clustering polymorphs of tau and IAPP fibrils with the CHEP algorithm. Progr. Biophys. and Mol. Biol. 160:16-25, 2021.
• Christiansen, M. Weiel, D. Born, A. Winkler, A. Schug, J. Reinstein. Modulation of stability and plasticity of the mavirus capsid building block is achieved by interlocked N-terminal arms. J. Mol. Biol. 433 (7), 166859, 2021.
  
• Weiel, M. Götz, A. Klein, D. Coquelin, R. Floca, A. Schug. FLAPS: Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions (submitted).