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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
HLA class I and II genotype of the NCI-60 cell lines
Adams, S., Robbins, F.-M., Chen, D., Wagage, D., Holbeck, S. L., Morse, H. C., Stroncek, D., & Marincola, F. M. (2005) · Journal of Translational Medicine · doi:10.1186/1479-5876-3-11 · Cited by 68
The opportunities of mining historical and collective data in drug discovery
Wassermann, A. M., Lounkine, E., Davies, J. W., Glick, M., & Camargo, L. M. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2014.11.004 · Cited by 34
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
