New genetic research technologies, such as DNA chips, enable scientists to evaluate simultaneously tissue samples from several patients, expressing thousands of genes. However, deciphering the vast amount of information derived, consisting of anything from 100,000 to 1,000,000 genetic figures, requires highly sophisticated data processing tools.
Addressing this and similar challenges may soon be easier thanks to Prof. Eytan Domany of the Weizmann Institute's Physics of Complex Systems Department and doctoral students Gad Getz and Erel Levine. The team has designed a unique mathematical system for analyzing genetic data based on a computer algorithm that "clusters" information into relevant categories. The algorithm searches simultaneously for clusters of "similar" genes and patients by evaluating the gene expression of tissue samples. (A gene's "expression" refers to the production level of the proteins it encodes.)
Reported in the October 17 issue of the Proceedings of the National Academy of Sciences (PNAS), the algorithm's most powerful feature is that it mimics unassisted learning. Unlike most automated "sorting" processes, in which a computer must be informed of the relevant categories in advance, the algorithm is analogous to human intuition (such as the ability to intuitively categorize images of animals and cars into proper classes). When given a clustering task, it analyzes the data, computes the degree of similarity among its components, and determines its own clustering criteria.
The new method makes use of a previous application by Domany and his colleagues based on a well-known physical phenomenon. When a granular magnet such as a magnetic tape is warm, its grains are highly disorganized. But upon cooling down, the magnets grains progressively organize themselves into well-ordered clusters. Using the statistical mechanics of granular magnets, Domany created an algorithm that can look for clusters in any data.
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Contact: Jeffrey J. Sussman
Jeffrey@acwis.org
212-895-7951
American Committee for the Weizmann Institute of Science
15-Oct-2000