The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Neural Computing in Cancer Drug Development: Predicting Mechanism of Action
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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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages
McNemar, Q. (1947) · Psychometrika · doi:10.1007/bf02295996 · Cited by 4,754
Large-scale neurocognitive networks and distributed processing for attention, language, and memory
Mesulam, M. (1990) · Annals of Neurology · doi:10.1002/ana.410280502 · Cited by 2,684
Cited by
Systematic variation in gene expression patterns in human cancer cell lines
Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., et al. (2000) · Nature Genetics · doi:10.1038/73432 · Cited by 2,088
Self-citationBoyd, M. R., & Paull, K. D. (1995) · Drug Development Research · doi:10.1002/ddr.430340203 · Cited by 1,572
Self-citationA review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274
Empirical Study on Applications of Data Mining Techniques in Healthcare
Kaur, H., & Wasan, S. K. (2006) · Journal of Computer Science · doi:10.3844/jcssp.2006.194.200 · Cited by 233
Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics
de Waal, L., Lewis, T. A., Rees, M. G., Tsherniak, A., Wu, X., Choi, P. S., Gechijian, L., Hartigan, C., Faloon, P. W., Hickey, M. J., et al. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.1984 · Cited by 96
Metadata
- Authors
- 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., & Paull, K. D.
- Venue
- Science
- Published
- 1992-10-16
- Volume
- 258
- Issue
- 5081
- Pages
- 447-451
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2083888914
- Cited by
- 312
- References
- 3
- FWCI
- 18.15
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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