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United States : $1.5 Million grant accepted by Brown U Researchers To Develop Better Analytical Tools for Big Data.

$1.5 million grant is received by Computer scientists at Brown University from the National Science Foundation and National Institutes of Health to develop new computer algorithms and statistical methods to analyze large, complex datasets. Eli Upfal, Computer science professor will be leading the research with fellow computer science professors Ben Raphael and Fabio Vandin.

A computer algorithm called HotNet was developed by the team, which provides helps isolate clusters of mutated genes that can cause cancer. Along with these this new grant, they will work to develop better computational tools to isolate genetic mutations that cause cancer and ensure the information is accurate.

As in recent years techniques for sequencing genes have become faster and cheaper, resulting in huge quantities of new data, developing new algorithms and statistical methods for analyzing big data is particularly important in the field of genomics. Recognition of important trends from all the data, while weeding out unimportant information is the challenge for researchers.

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.

New methods will be developed by Upfal and his team for recognising 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.

As their ultimate goal is to evelop computer algorithms and statistical methods that can be applied to other big data sets as well, the analytical tools Upfal and his team are developing are focused on genomics data.

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Publication:Mena Report
Date:Oct 11, 2012
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