Intel is driving toward a day when cancer patients routinely have their tumor DNA sequenced and receive precision treatment plans based on their unique biomolecular profile—all within 24 hours. We call this vision All in One Day, and we believe that with the right blend of industry-wide commitment, innovation, and collaboration, we can deliver on that vision in 2020.
All in One Day isn’t an endpoint, though. I believe it’s also a step toward a world in which life science researchers use ultra-sophisticated 3-D models to simulate the workings of the human body and predict health outcomes. As Dr. Jason Paragas, director of innovation at Lawrence Livermore National Lab, likes to say, “We’d never ask an engineer to build a bridge or design an airplane without modeling how it’s going to perform in the real world. But doctors do the equivalent every day.”
If we can empower researchers with advanced biomedical models and simulations, we stand to transform the practice of medicine. Building on the genomics revolution, we may be able to take much more guesswork out of medicine and dramatically expand the universe of available diagnostics, treatments, and preventive approaches.
It’s going to take massive increases in computing performance to support these breakthroughs. In the United States, the President’s National Strategic Computing Initiative (NSCI) aims to advance the technologies needed for computers that are 100 times more powerful than today’s most capable supercomputers. Other nations are moving forward with similar initiatives.
I recently worked with two of my HPC colleagues to develop a whitepaper that explores precision medicine and discusses Intel’s role in enabling it.
We talk about the central role of Intel® Scalable System Framework and its ability to support the convergence of HPC modeling/simulation, health analytics, machine learning, and visualization that precision medicine will require.
We touch on key technology innovations as well as collaborations with life science leaders to create open source platforms, tools, applications, and algorithms for precision medicine.
And we note that the advances provided by these extreme-scale computers will help us address critical challenges like climate change and renewable energy sources as well as enabling progress toward predictive biology and precision medicine.
I hope you’ll read the whitepaper and share your thoughts. What opportunities do you see for life sciences computing to transform biomedicine? What roadblocks are in the way?