The U.S. National Science Foundation (NSF) has awarded University of Texas at Arlington (UTA) professor Junzhou Huang a grant to develop computing tools enabling image-omics data to be integrated into files small enough for researchers to better anticipate patient life expectancy and the best medical treatment pathways using current computing technology.
Image-omics data includes image data, such as pathology or radiology images, and "omics" data, such as data from genomics, proteomics, or the study of proteins or metabolomics, captured from the same patient.
The resolution of image-omics data is so high a single piece of data may constitute one terabyte.
"Access to different multiple-source data will allow doctors and scientists to develop better treatments for patients," Huang says. "There is no current research in data mining to integrate very complex image-omics data, but if we are successful, scientists will have a much broader base of information to draw upon when seeking cures for diseases such as cancer."
Huang is the director of UTA's Scalable Modeling and Imaging and Learning Lab, which creates scalable models and algorithms for data-intensive applications in high-performance computing. His research highlights how UTA is committed to advancing work in data-driven discovery under the Strategic Plan 2020: Bold Solutions/Global Impact.
Anand Puppala, associate dean for research for UTA's College of Engineering, says Huang's NSF grant "will allow him to make innovative, potentially life-altering discoveries that will benefit science and medicine, as well as the community."
From UTA News Center
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