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| http://dx.doi.org/10.3389/fdata.2022.838097 | DOI Listing |
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Exascale simulations and beyond.
Curr Opin Struct Biol
December 2024
Los Alamos National Laboratory, United States and New Mexico Consortium, New Mexico, United States. Electronic address:
: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations.
Front Neuroinform
April 2024
Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands.
Introduction: simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions.
View Article and Find Full Text PDFLast week, reflected on major achievements in science in 2023, from weight loss drugs and a malaria vaccine to exascale computing and advances in artificial intelligence. These are all impressive developments and provide yet more testimony to the power of science to continually expand the quality of our lives while deepening our understanding of the world. Even so, it's hard to end the year without some worries about 2024.
View Article and Find Full Text PDFBiophysics at the dawn of exascale computers.
Biophys J
July 2023
UMR 7019, Université de Lorraine, Laboratoire International Associé CNRS, Vandœuvre-lès-Nancy, France; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois; Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois.
Empowering digital pathology applications through explainable knowledge extraction tools.
J Pathol Inform
September 2022
Department of Information Engineering, University of Padua, Padua, Italy.
Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET).
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