Mount Sinai Health System has received a $2 million grant from the U.S. Department of Health and Human Services that will help it big another “big omics data engine” – this one known as BODE 2 and nearly twice as powerful as the first one.
WHY IT MATTERS
“Supercomputers have become essential in biomedical scientific discovery, and Mount Sinai has been a leader on this front, making investments in computational and data science that are advancing our understanding of and ability to treat complex diseases,” explained Dr. Dennis S. Charney, president for academic affairs at Mount Sinai Health System, a statement announcing the new Lenovo ThinkSystem SR360.
BODE 2 will feature 3,840 Intel Cascade Lake cores, according to the health system, with 15 terabytes of memory, 14 petabytes of raw storage and 11 petabytes of usable storage. That will be good for about 28 million core compute hours per year at a frequency of 2.6 GHz, with a peak speed of 220 teraflops per second – about twice what the first BODE (which was used by more than five-dozen translational researchers at Mount Sinai and collaborators at 75 external institutions) was capable of.
“With BODE 2, we are renewing our commitment to push the boundaries of scientific research, tackle questions that we did not previously have the computational power to take on, and achieve breakthroughs that transform clinical care worldwide,” said Charney.
Among the clinical research projects Mount Sinai hopes to tackle with the supercomputer, set to go online later this year: Leading-edge Alzheimer’s research – thanks to BODE 2’s storage capability for whole-genome-sequencing datasets (from more than 10,000 patients) and its ability to provider processing power (perhaps as much as 12 million compute hours) to analyze that data using machine learning.
Another Mount Sinai initiative, the Trans-Omics for Precision Medicine Program, or TOPMed, will benefit from 1.75 petabytes of storage necessary for whole-genome-sequencing – as well as other omics, molecular, behavioral, imaging, environmental, and clinical data for this unprecedented exploration of the biological causes underlying heart, blood, lung, and sleep disorders.
THE LARGER TREND
Mount Sinai has been making news this year with an array of innovations around personalized medicine and machine learning. In March, the health system, in collaboration with Hasso Plattner Institute, launched new digital health institute, designed to advance biomedical engineering, artificial intelligence and more, and to develop new predictive and preventive digital health tools for precision medicine, population health and value-based care.
And in June, one Mount Sinai data scientist showed how clinicians and case managers there are using natural language processing algorithms to gain access to valuable social determinant data captured in unstructured EHR progress notes, procedure and consultation data, discharge summaries and more.
ON THE RECORD
“This new supercomputer will enable us to mine deep databases of genomic and clinical information using machine-learning approaches to propel the personalized medicine of today into better medicine tomorrow,” said Eimear Kenny, director of Mount Sinai’s Center for Genomic Health, and a Principal Investigator of the TOPMed Program, in a statement. “The technology will help fuel innovative research programs to further our understanding of disease progression and management.”
“Computing capability of this size and speed is not available widely, and Mount Sinai’s investment in building this infrastructure will translate into more robust genetics and population analysis, gene expression, machine learning, and structural and chemical biology investigations, and result in new insights and advances in a wide range of diseases including Alzheimer’s, autism, influenza, prostate cancer, schizophrenia, and substance use disorders,” said Patricia Kovatch, senior associate dean for Scientific Computing and Data Science at the Icahn School of Medicine at Mount Sinai.
Healthcare IT News is a publication of HIMSS Media.