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Computer scientists develop tool for mining genomic data

Equipped with cutting-edge techniques to track the activity of tens of thousands of genes in a single experiment, biologists now face a new challenge - determining how to analyze this tidal wave of data. Stanford Associate Professor of Computer Science Daphne Koller and her colleagues have come to the rescue with a strategic approach that reduces the trial-and-error aspect of genetic sequence analysis.

''What we're developing is a suite of computational tools that take reams of data and automatically extract a picture of what's happening in the cell,'' says Koller. ''It tells you where to look for good biology.''

Koller presented her statistical approach for mining genomic data at a Feb. 14 symposium - ''Machine Learning in the Sciences'' - at the annual meeting of the American Association for the Advancement of Science (AAAS) in Seattle.

Several years ago, before Koller came onto the scene, a new generation of high-throughput assays revolutionized molecular biology. In the most stunning example of this technology, scientists began using thumbnail-sized ''gene chips'' to monitor the activities of thousands of genes at once. In October 2003, Santa Clara-based Affymetrix took this breakthrough to a new level when it began marketing whole-genome chips packed with all 30,000 to 50,000 known human genes. Genome chips can reveal, for instance, that in kidney cells treated with a certain drug, 116 genes spring into action while another 255 get shut off.

But this state-of-the-art DNA microarray technology provides only a single snapshot of the cell. ''It's a very partial view,'' Koller says.

What scientists really want to know is how groups of genes work together to control specific biological processes, such as muscle development or cancer progression. Unraveling these regulatory networks - for example, determining that Gene A gets activated by Gene B but repressed by Gene C - is a daunting task.

Sifting through whopping amou
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Contact: Dawn Levy
dawnlevy@stanford.edu
650-725-1944
Stanford University
14-Feb-2004


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