Topic: Biology/Biomedicine

The White House announced the COVID-19 HPC Consortium to provide access to the world’s most powerful HPC resources that can advance the pace of scientific discovery in the fight to stop the virus.

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A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

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The paper describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins and interactions with lipids.

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LLNL researchers and colleagues are using machine learning as a virtual magnifying glass to study interesting regions of RAS protein/lipid simulations in higher detail.

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The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research.

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As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways.

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LLNL and the Livermore Lab Foundation may establish a consortium to leverage the Lab’s computing capabilities to identify causal factors for amyotrophic lateral sclerosis, or Lou Gehrig’s disease.

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NFL officials visited LLNL to hear how national labs are using HPC and AI to advance scientific understanding of traumatic brain injury.

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Computer scientists, biomedical engineers, cancer biologists, and bioinformaticians from 8 DOE labs, health agencies, and universities advance cancer research through computation.

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The “best in class” award was for the Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium, which aims to use HPC to accelerate drug development.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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LibRom is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

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Livermore researchers are enhancing HARVEY, an open-source parallel fluid dynamics application designed to model blood flow in patient-specific geometries.

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The Livermore Metagenomic Analysis Toolkit (LMAT) is a genome sequencing technology that helps accelerate the comparison of genetic fragments with reference genomes and improve the accuracy of the results as compared to previous technologies. It tracks approximately 25 billion short sequences and is currently being evaluated for potential operational use in global biosurveillance and microbial forensics by various federal agencies.

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