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Weinstein JN, Kohn KW, Grever MR, et al. Neural computing in cancer drug development: predicting mechanism of action. Science. 1992;258(5081):447-51doi: 10.1126/science.1411538.
Weinstein, J. N., Kohn, K. W., Grever, M. R., Viswanadhan, V. N., Rubinstein, L. V., Monks, A. P., Scudiero, D. A., Welch, L., Koutsoukos, A. D., & Chiausa, A. J. (1992). Neural computing in cancer drug development: predicting mechanism of action. Science (New York, N.Y.), 258(5081), 447-51. https://doi.org/10.1126/science.1411538
Weinstein, J N, et al. "Neural computing in cancer drug development: predicting mechanism of action." Science (New York, N.Y.) vol. 258,5081 (1992): 447-51. doi: https://doi.org/10.1126/science.1411538
Weinstein JN, Kohn KW, Grever MR, Viswanadhan VN, Rubinstein LV, Monks AP, Scudiero DA, Welch L, Koutsoukos AD, Chiausa AJ. Neural computing in cancer drug development: predicting mechanism of action. Science. 1992 Oct 16;258(5081):447-51. doi: 10.1126/science.1411538. PMID: 1411538.
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Science. 1992 Oct 16;258(5081):447-51. doi: 10.1126/science.1411538.

Neural computing in cancer drug development: predicting mechanism of action.

Science (New York, N.Y.)

J N Weinstein, K W Kohn, M R Grever, V N Viswanadhan, L V Rubinstein, A P Monks, D A Scudiero, L Welch, A D Koutsoukos, A J Chiausa

Affiliations

  1. Laboratory of Mathematical Biology, National Cancer Institute, Bethesda, MD 20892.

PMID: 1411538 DOI: 10.1126/science.1411538

Abstract

Described here are neural networks capable of predicting a drug's mechanism of action from its pattern of activity against a panel of 60 malignant cell lines in the National Cancer Institute's drug screening program. Given six possible classes of mechanism, the network misses the correct category for only 12 out of 141 agents (8.5 percent), whereas linear discriminant analysis, a standard statistical technique, misses 20 out of 141 (14.2 percent). The success of the neural net indicates several things. (i) The cell line response patterns are rich in information about mechanism. (ii) Appropriately designed neural networks can make effective use of that information. (iii) Trained networks can be used to classify prospectively the more than 10,000 agents per year tested by the screening program. Related networks, in combination with classical statistical tools, will help in a variety of ways to move new anticancer agents through the pipeline from in vitro studies to clinical application.

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