Over a the University of Texas at Austin, Marc Airhart writes that researchers are using TACC supercomputers to better understand the physics behind the spread of cancer. “Having a physicist working on cancer can provide a new perspective into how a tumor evolves,” said Abdul Malmi-Kakkada, a postdoctoral researcher who led the project, along with postdoctoral researcher Xin Li, and professor and chair of chemistry Dave Thirumalai. “And rather than only looking at genetics or biology, trying to attack the problem of cancer from different perspectives can hopefully lead to a better understanding.”
Fighting Cancer with Deep Learning at Scale with the CANDLE Project
January 12, 2018 by
In this episode of Let’s Talk Exascale, Mike Bernhardt discusses the CANDLE project for cancer research with Rick Stevens from Argonne National Lab. The CANcer Distributed Learning Environment (CANDLE) is an ECP application development project targeting new computational methods for cancer treatment with precision medicine.
Podcast: Targeting Cancer with 3D Modeling and Simulation
August 6, 2017 by
In this podcast, Oregon State University Associate Professor Eugene Zhang and Assistant Professor Yue Zhang describe their research to help medical doctors better target cancerous tumors by using 3D modeling and simulation. “What we are hoping to achieve is we will get adaptive treatment plan and individualized for each patient. What we are trying to do here that is novel is we want to include bio mechanical modeling the simulations we want to include the tensor visualization on the material stress tensors.”
BSC Comparing Algorithms that Search for Cancer Mutations
July 18, 2017 by
Eduard Porta-Pardo from BSC has undertaken the first ever comparative analysis of sub-gene algorithms that mine the genetic information in cancer databases. These powerful data-sifting tools are helping untangle the complexity of cancer, and find previously unidentified mutations that are important in creating cancer cells. “Finding new cancer driver genes is an important goal of cancer genome analysis,” adds Porta-Pardo. This study should help researchers understand the advantages and drawbacks of sub-gene algorithms used to find new potential drug targets for cancer treatment.
Supercomputing New Tools for Cancer Detection
July 4, 2017 by
“In the future, though, it may be possible to diagnose cancer much earlier using more sensitive body scans, new types of biomarker tests, and even nano-sensors working in the bloodstream. Experimenting with these techniques in cancer patients or healthy individuals is difficult and potentially unethical. But scientists can test these technologies virtually using supercomputers to simulate the dynamics of cells and tissues.”
Supercomputing Cancer Data for Treatment Clues
June 9, 2017 by
In this video, researchers use TACC supercomputers in war against cancer. “Next-generation sequencing technology allows us to observe genomes and their activity in unprecedented detail,” he said. “It’s also making a lot of biomedical research increasingly computational, so it’s great to have a resource like TACC available to us.”
Supercomputing High Energy Cancer Treatments
May 10, 2017 by
Over at TACC, Aaron Dubrow writes that researchers are using TACC supercomputers to improve, plan, and understand the basic science of radiation therapy. “The science of calculating and assessing the radiation dose received by the human body is known as dosimetry – and here, as in many areas of science, advanced computing plays an important role.”
AI Technology from China Helps Radiologists Detect Lung Cancer
May 8, 2017 by
Today Infervision introduced its innovative, deep learning solution to help radiologists identify suspicious lesions and nodules in lung cancer patients faster than ever before. The Infervision AI platform is the world’s first to reshape the workflow of radiologists and it is already showing dramatic results at several top hospitals in China.
Turning AI Against Cancer at the Data Science Bowl
May 2, 2017 by
Today Booz Allen Hamilton and Kaggle today announced the winners of the third annual Data Science Bowl, a competition that harnesses the power of data science and crowdsourcing to tackle some of the world’s toughest problems. This year’s challenge brought together nearly 10,000 participants from across the world. Collectively they spent more than an estimated 150,000 hours and submitted nearly 18,000 algorithms—all aiming to help medical professionals detect lung cancer earlier and with better accuracy.
Podcast: Supercomputing Cancer Research and the Human Brain
January 26, 2017 by
In this WUOT podcast, Jack Wells from ORNL describes how the Titan supercomputer helps advance science. “The world’s third-most powerful supercomputer is located in Oak Ridge, and though it bears the imposing name TITAN, its goals and capabilities are more quotidian than dystopian. After that, WUOT’s Megan Jamerson tells us about a project at ORNL that uses TITAN to help humans digest vast sums of information from medical reports. If successful, the project could create new understandings about the demographics of cancer.”
