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Brown U Researchers Awarded $1.5 Million Grant To Develop Better Analytical Tools for Big Data
Computer scientists at Brown University have been granted $1.5 million from the National Science Foundation and National Institutes of Health to develop new computer algorithms and statistical methods to analyze large, complex datasets.
Computer science professor Eli Upfal will lead the research with fellow computer science professors Ben Raphael and Fabio Vandin. Last year the team developed a computer algorithm called HotNet that helps isolate clusters of mutated genes that can cause cancer. With this new grant, they will work to develop better computational tools to isolate genetic mutations that cause cancer and ensure the information is accurate.
Developing new algorithms and statistical methods for analyzing big data is particularly important in the field of genomics because in recent years techniques for sequencing genes have become faster and cheaper, resulting in huge quantities of new data. The challenge for researchers is sifting through all the data to identify important trends while weeding out unimportant information.
“These datasets have all the good and bad properties of Big Data,” said Upfal in a prepared statement. “They’re big, noisy, and require very complicated statistical analysis to obtain useful information.”
Upfal and his team plan to develop new methods to identify with statistical certainty which of the mutations in cancer cells actually contribute to cancer growth and which don't and develop analytical tools to ensure that the lab tools used to sequence genes have recorded the information accurately.
Although the analytical tools Upfal and his team are developing are focused on genomics data, their ultimate goal is to develop computer algorithms and statistical methods that can be applied to other big data sets as well.
Leila Meyer is a technology writer based in British Columbia. She can be reached at firstname.lastname@example.org.