New Mathematical Method for Analyzing Genetic Data

Scientists have developed a mathematical method for analyzing genetic data that could improve the reliability of research findings, make the interpretation of experimental data more accurate, and accelerate the pace of research.

Much of the research conducted on genes is done by using gene expression microarrays, which have improved the effectiveness of genetics research. However, this research has proven time consuming and expensive because it generates relatively small data sets and therefore, "noisy,” or unreliable results.

The current method used to analyze microarray data is based on an arbitrary formulation. Researchers have developed a new algorithm that replaces this method with a mathematically rigorous correlation coefficient of two gene expression vectors, based on James-Stein shrinkage estimators. The new algorithm has been shown in research to correct for many kinds of errors.

"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,” explained Bud Mishra, Ph.D., professor of computer science and mathematics at the Courant Institute of Mathematical Sciences at New York University (NY, USA), who led the research. "This is not about data mining, or computation dealing with large amounts of data; it's about developing a better, more intelligent way of looking at things.”




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