The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
A survey of current trends in computational drug repositioning
Open accessAbstract
Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
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
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
Genomic responses in mouse models poorly mimic human inflammatory diseases
Seok, J., Warren, H. S., Cuenca, A. G., Mindrinos, M. N., Baker, H. V., Xu, W., Richards, D. R., McDonald-Smith, G. P., Gao, H., Hennessy, L., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222878110 · Cited by 3,005
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
Genetics of rheumatoid arthritis contributes to biology and drug discovery
the RACI consortium, Okada, Y., the GARNET consortium, Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., et al. (2014) · Nature · doi:10.1038/nature12873 · Cited by 2,310
Yıldırım, M. A., Goh, K.-I., Cusick, M. E., Barabási, A.-L., & Vidal, M. (2007) · Nature Biotechnology · doi:10.1038/nbt1338 · Cited by 1,708
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667
Cited by
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Self-citationModeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., & Leskovec, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty294 · Cited by 1,501
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675
Jarada, T. N., Rokne, J. G., & Alhajj, R. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00450-7 · Cited by 384
A review of network-based approaches to drug repositioning
Lotfi Shahreza, M., Ghadiri, N., Mousavi, S. R., Varshosaz, J., & Green, J. R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx017 · Cited by 330
Metadata
- Authors
- Li, J., Zheng, S., Chen, B., Butte, A. J., Swamidass, S. J., & Lu, Z.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-03-31
- Volume
- 17
- Issue
- 1
- Pages
- 2-12
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2151357092
- Cited by
- 555
- References
- 103
- FWCI
- 46.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A survey of current trends in computational drug repositioning. https://app.cochat.ai/papers/10.1093/bib/bbv020
- The paper
- Li, J., Zheng, S., Chen, B., Butte, A. J., Swamidass, S. J., & Lu, Z. (2016). A survey of current trends in computational drug repositioning. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbv020
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.
