( >The NYU algorithm estimates the locati...)NYU algorithm enhances ability to detect cancer genes

>The NYU algorithm estimates the location of tumor suppressor genes by analyzing segmental deletions in the genomes from cancer patients and the spatial relation of the deleted segments to any specific genomic interval. Since the gene-chip consists of many "probes"--each one characterizing an almost unique word and its location in the already-sequenced human genome--by combining these probe-measurements, one can estimate if an important genomic segment is missing. By analogy, this process is akin to guessing if a new edition of a book is missing an important paragraph by checking if some of the important key words in that paragraph are missing from the index of the new edition. The new algorithm computes a multipoint score for all intervals of consecutive probes, and the score reflects how well the deletion of that genomic interval may explain the cancer in these patients. In other words, the computed score measures how likely it is for a particular genomic interval to be a tumor suppressor gene implicated in the disease. In order to validate their algorithm, the authors produced a high fidelity in silico model of cancer, and checked how well they can detect the right genes, as they modified various parameters of the model in an adversarial manner. Encouraged by the success of their in silico study, they applied the algorithm to currently available patient data, and discovered that they were able to detect many genes that were already known in the literature, but also, several others that are statistically equally significant, but not found by the earlier studies.

The findings also showed that the algorithm may be applied to a wider class of problems--including the detection of oncogenes, which promote the growth of cancer when they are mutated or overexpressed. As the technology and the statistical algorithms of this nature keep improving in cost and accuracy, it will prove useful in finding good biomarkers, drug discovery, disease diagnosis, and choosing c
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Source:New York University

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