Researchers at New York Universitys Courant Institute of Mathematical Sciences have developed a new algorithm that can lead to more accurate detection of cancer genes than previous versions. The algorithm, published in the latest issue of the Proceedings of the National Academy of Sciences (PNAS), can also be applied to the multiple biomedical technologies (e.g., different kinds of micro-arrays) used to analyze cancer patients genomes.
Headed by NYU Professor Bud Mishra, the research team developed the algorithm to detect the genetic differences between normal cells and cancer cells. Its application reveals several excess as well as missing copies of DNA segments associated with various forms of cancer and ultimately, points to locations of both oncogenes and tumor suppressor genes. In addition, the algorithm can be used to account for the varied genomes present across human population.
An earlier version of the algorithm as well as several other competing algorithms were capable of dealing with only cancer data or only polymorphism data and were unable to separate variations in cancerous and non-cancerous genes in a single framework.
James Devitt | EurekAlert!
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