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NYU team develops enhanced algorithm for detecting changes in cancer genomes

Researchers at New York University's 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.

Mishra's team, which forms NYU's Bioinfomatics Group, has previously examined new genomic technology for mapping and sequencing with single molecules, models of genome evolution, and computational and systems biology models of biological processes like apoptosis, cell divisions, and others involved in cancer.

Two senior research scientists from the Bioinformatics Group, Raoul-Sam Daruwala and Archisman Rudra, collaborated with Mishra to devise the algorithm and create its software implementation. Daruwala, Rudra, and Mishra were joined in the study by colleagues from Cold Spring Harbor Laboratory and NYU School of Medicine.

The algorithm runs through Valis, a software environment developed by Mishra and Courant's Salvatore Paxia in 2001, with the help of a New York State Office of Science, Tec
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Contact: James Devitt
james.devitt@nyu.edu
212-998-6808
New York University
8-Nov-2004


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