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Making the most of microarray data

Nature Genetics volume 24, pages 204–206 (2000)Cite this article

The impact of microarray technology on biology will depend on computational methods of data analysis. A supervised computer-learning method using support vector machines predicts gene function from expression data—and shows promise.

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Figure 1: A support vector machine (SVM) is a computational entity that accepts positive and negative training examples of a topic to be learned.
Figure 2: An untrained support vector machine (a) is trained with positive examples (green) and negative examples (red) to build a trained machine ( b; Fig. 1) that can take an unknown object (white) and determine whether or not it is similar to the training set.

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Authors and Affiliations

  1. Laboratory of Computational Genomics, The Rockefeller University, 1230 York Ave, New York, 10021, New York, USA

    Terry Gaasterland & Stefan Bekiranov

  2. Terry Gaasterland & Stefan Bekiranov

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  1. Terry Gaasterland
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  2. Stefan Bekiranov
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Gaasterland, T., Bekiranov, S. Making the most of microarray data. Nat Genet 24, 204–206 (2000). https://doi.org/10.1038/73392

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