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Podcast: A Codebase for Deep Learning Supercomputers to Fight Cancer

In this Let’s Talk Exascale podcast, Gina Tourassi from ORNL describes how the CANDLE project is setting the stage to fight cancer with the power of Exascale computing. “Basically, as we are leveraging supercomputing and artificial intelligence to accelerate cancer research, we are also seeing how we can drive the next generation of supercomputing.”

XSEDE Supercomputers Advance Skin Cancer Research

In this TACC podcast, UC Berkeley scientists describe how they are using powerful supercomputers to uncover the mechanism that activates cell mutations found in about 50 percent of melanomas. “The study’s computational challenges involved molecular dynamics simulations that modeled the protein at the atomic level, determining the forces of every atom on every other atom for a system of about 200,000 atoms at time steps of two femtoseconds.”

Exascale CANDLE Project to Fight Against Cancer

The CANcer Distributed Learning Environment, or CANDLE, is a cross-cutting initiative of the Joint Design of Advanced Computing Solutions for Cancer collaboration and is supported by DOE’s Exascale Computing Project (ECP). CANDLE is building a scalable deep learning environment to run on DOE’s most powerful supercomputers. The goal is to have an easy-to-use environment that can take advantage of the full power of these systems to find the optimal deep-learning models for making predictions in cancer.

Ai allows for identification of new cancer genes

Researchers at the Barcelona Supercomputing Centre have created a new artificial intelligence-based computational method that accelerates the identification of new genes related to cancer. Prof. Pržulj highlights that this new method to analyze cells “enables the identification of perturbed genes in cancer that do not appear as perturbed in any data type alone. This discovery emphasizes the importance of integrative approaches to analyze biological data and paves the way towards comparative integrative analyzes of all cells.”

BSC fosters EUCANCan Project to share and reuse cancer genomic data worldwide

Today the Barcelona Supercomputing Center announced it will foster the EUCANCan project to allow both research and cancer treatments to be shared and re-used by the European and Canadian scientific community. As demonstrated by earlier work, research that merges and reanalyzes  biomedical data from different studies significantly increases the chances of new discoveries.

Supercomputing How Cancer Spreads through Superdiffusion

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

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

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

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

“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.”