Topic: Biology/Biomedicine

From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.

LLNL has turned to AMD and Penguin Computing to upgrade a supercomputer to help in the fight against the novel coronavirus. The computer's name is... Corona. The 2018 system, named for the total solar eclipse of 2017, will nearly double in peak performance to 4.5 peak petaflops.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.

In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.

The Accelerating Therapeutics for Opportunities in Medicine consortium, of which LLNL is part, announced the U.S. Department of Energy’s Argonne, Brookhaven and Oak Ridge national labs are joining the consortium to further develop ATOM’s AI-driven drug discovery platform.

LLNL and IBM research on deep learning models to accurately diagnose diseases from x-ray images won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.

A multi-institutional team including LLNL is using Summit, America’s fastest supercomputer, to understand how certain proteins signal body cells to reproduce uncontrollably, triggering cancer.

A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for HPC-Based COVID-19 Research.

At the 2020 LLNL Computing Expo, Jim Brase outlined the Lab’s predictive biology efforts for new therapeutics, HPC capabilities, and making those resources available to researchers.

Ruby, a 6 petaFLOP cluster, will be used for in support of the NNSA’s stockpile stewardship mission, open science, and the search for therapeutic drugs and designer antibodies against SARS-CoV-2.

Funded by the CARES Act, LLNL's new computing cluster, Mammoth, will be used to perform genomics analysis, nontraditional simulations, and graph analytics required by scientists working on COVID-19s.

The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

Funding by the CARES Act enabled LLNL and industry partners to more than double the speed of the Corona supercomputing cluster to in excess of 11 petaFLOPS of peak performance.

LLNL pairs 3D-printed human brain vasculature with computational flow simulations to understand tumor cell attachment to blood vessels, a step in secondary tumor formation during cancer metastasis.

LLNL and Duke University combined 3D bioprinting and computational flow models to analyze the physics behind circulating tumor cell behavior and the cells’ attachment to the vascular endothelium.

LLNL researchers and collaborators have combined machine learning, 3D printing, and HPC simulations to accurately model blood flow in the aorta.

This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

A team led by an LLNL computer scientist proposes a deep learning approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

To help accelerate discovery of therapeutic antibodies or antiviral drugs for SARS-CoV-2, LLNL has launched a searchable data portal to share its COVID-19 research with scientists and the public.

LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50.

LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2.

LLNL has infrastructure, unique research capabilities, and a dedicated team of scientists and engineers supporting the fight against COVID-19.

LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.