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NYU algorithm enhances ability to detect cancer genes

Researchers at New York University's Courant Institute of Mathematical Sciences have developed a new algorithm that enhances the ability to detect a cancer gene, and have applied their algorithm to map the set of tumor-suppressor genes involved in lung cancer. The algorithm uses data from Affymetrix's gene-chips that can scan hundreds of patients' genomes to find gains and losses in gene-copies. The findings will appear in the July issue of the American Journal of Human Genetics.

The study was conducted by Iuliana Ionita, a PhD student in computer science, Raoul-Sam Daruwala, a former research scientist from Courant Bioinformatics group and currently at Google, and Courant Professor Bud Mishra. Mishra is a professor of computer science and mathematics at the Courant Institute and also has an appointment in the Department of Cell Biology at NYU's School of Medicine.

Previous research has found that certain gene-chips--a technology that allows the genome-wide screening for mutations in genes or changes in gene expressions all at once--shed light on genes and mechanisms involved in the onset and spread of cancer. Specifically, chromosomal segments, when deleted in a single or both copies of genomes of a group of cancer patients, point to locations of tumor suppressor genes implicated in the cancer. The NYU study focused on automatic methods for reliable detection of such genes, their locations, and their boundaries. For this purpose, the NYU scientists sought to devise an efficient and novel statistical algorithm to map tumor suppressor genes using a multi-point statistical score function. Their algorithm is unique in that it exploits the high resolution of gene-chips and prior biological models through Bayesian statistics in order to optimally pinpoint the genes involved in the cancer, even when these genomes may have many other unrelated deletions, which happen as "collateral damage" to the genomes as the cancer progresses to an advanced stage. <
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Contact: James Devitt
james.devitt@nyu.edu
212-998-6808
New York University
9-May-2006


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