NYU scientists develop more accurate mathematical method to analyze genetic data

A multi-disciplinary group of scientists at New York University, led by Bud Mishra, professor of computer science and mathematics at NYU's Courant Institute of Mathematical Sciences, has developed a mathematical method for analyzing genetic data that could drastically improve the reliability of research findings. As a result, scientists using the new method can vastly improve the accuracy of the interpretation of experimental data, saving valuable resources and potentially accelerating the pace of research.

Since the human genome was fully mapped, the ongoing challenge for scientists has been to analyze exactly how the different genes and their products interact to give individuals their unique traits or give rise to genetic diseases such as cancer. Much of this research is done through the use of gene expression microarrays or gene chips which grid down DNA on a solid surface in order to monitor interactions among hundreds or thousands of genes simultaneously.

While microarrays have vastly improved the effectiveness of genetics research, these experiments have proven time consuming and expensive due to the fact that they generate "relatively small" data sets, and therefore "noisy" or unreliable results. Mishra's team's new method for data analysis introduces a novel way of statistically manipulating the data to reduce the effect of noise, potentially reducing the need to generate additional data, as well as the time wasted from inaccurate results. The finding was published in the July 28 issue of the Proceedings of the National Academy of Sciences (PNAS).

"In spite of the fact that mathematics has been around for thousands of years, it is extremely new to biology, and our research in this area has focused on how best to leverage quantitative thinking in order to improve biological research," said Mishra. "This is not about data mining, or computation dealing with large amounts of data; it's about developing a better, more intelligen

Contact: Shonna Keogan
New York University

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