The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data
Open accessAbstract
A systematic computational method predicts new uses for existing drugs by integrating public gene expression signatures of drugs and diseases.
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
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
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
Drug target identification using side-effect similarity
Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008) · Science · doi:10.1126/science.1158140 · Cited by 1,248
Cited by
Drug repurposing: progress, challenges and recommendations
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.168 · Cited by 4,718
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
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
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017) · eLife · doi:10.7554/elife.26726 · Cited by 666
Network-based in silico drug efficacy screening
Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L. (2016) · Nature Communications · doi:10.1038/ncomms10331 · Cited by 625
Jarada, T. N., Rokne, J. G., & Alhajj, R. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00450-7 · Cited by 384
Applications of Connectivity Map in drug discovery and development
Qu, X. A., & Rajpal, D. K. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.07.017 · Cited by 235
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A.-L., Baranzini, S., Brunak, S., Chung, K. F., Federoff, H. J., Gavin, A.-C., Meehan, R. R., et al. (2014) · Genome Medicine · doi:10.1186/s13073-014-0082-6 · Cited by 194
Towards understanding brain-gut-microbiome connections in Alzheimer’s disease
Xu, R., & Wang, Q. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0307-y · Cited by 175
Design of efficient computational workflows for in silico drug repurposing
Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., Labat, I., & Zhavoronkov, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2016.09.019 · Cited by 150
Campbell, J. D., McDonough, J. E., Zeskind, J. E., Hackett, T. L., Pechkovsky, D. V., Brandsma, C.-A., Suzuki, M., Gosselink, J. V., Liu, G., Alekseyev, Y. O., et al. (2012) · Genome Medicine · doi:10.1186/gm367 · Cited by 128
Taubes, A., Nova, P., Zalocusky, K. A., Kosti, I., Bicak, M., Zilberter, M. Y., Hao, Y., Yoon, S. Y., Oskotsky, T., Pineda, S., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00122-7 · Cited by 118
Self-citationqSVA framework for RNA quality correction in differential expression analysis
Jaffe, A. E., Tao, R., Norris, A. L., Kealhofer, M., Nellore, A., Shin, J. H., Kim, D., Jia, Y., Hyde, T. M., Kleinman, J. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617384114 · Cited by 114
Drug–target interaction predication via multi-channel graph neural networks
Li, Y., Qiao, G., Wang, K., & Wang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab346 · Cited by 112
Linking Phenotypes and Modes of Action Through High-Content Screen Fingerprints
Reisen, F., Sauty de Chalon, A., Pfeifer, M., Zhang, X., Gabriel, D., & Selzer, P. (2015) · ASSAY and Drug Development Technologies · doi:10.1089/adt.2015.656 · Cited by 76
Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T. (2017) · JACC: Basic to Translational Science · doi:10.1016/j.jacbts.2016.11.010 · Cited by 83
Self-citationHill, M. A., & Gammie, S. C. (2022) · Scientific Reports · doi:10.1038/s41598-022-22179-z · Cited by 56
Zeng, B., Glicksberg, B. S., Newbury, P., Chekalin, E., Xing, J., Liu, K., Wen, A., Chow, C., & Chen, B. (2021) · Nature Protocols · doi:10.1038/s41596-020-00430-z · Cited by 48
Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing
Chan, J., Wang, X., Turner, J. A., Baldwin, N. E., & Gu, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz006 · Cited by 50
Targeting SALL4 by entinostat in lung cancer
Yong, K. J., Li, A., Ou, W.-B., Hong, C. K. Y., Zhao, W., Wang, F., Tatetsu, H., Yan, B., Qi, L., Fletcher, J. A., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.12251 · Cited by 41
Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders
Schadt, E. E., Buchanan, S., Brennand, K. J., & Merchant, K. M. (2014) · Frontiers in Pharmacology · doi:10.3389/fphar.2014.00252 · Cited by 39
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
Can cancer researchers accurately judge whether preclinical reports will reproduce?
Benjamin, D., Mandel, D. R., & Kimmelman, J. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2002212 · Cited by 32
Konuma, T., Ogawa, K., & Okada, Y. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab049 · Cited by 22
Using Chemical-Induced Gene Expression in Cultured Human Cells to Predict Chemical Toxicity
Liu, R., Yu, X., & Wallqvist, A. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00287 · Cited by 8
Prediction and identification of synergistic compound combinations against pancreatic cancer cells
KalantarMotamedi, Y., Choi, R. J., Koh, S.-B., Bramhall, J. L., Fan, T.-P., & Bender, A. (2021) · iScience · doi:10.1016/j.isci.2021.103080 · Cited by 8
A gene expression signature of emphysematous lung destruction and its reversal by the tripeptide GHK
Campbell, J. D., McDonough, J. E., Zeskind, J. E., Hackett, T. L., Pechkovsky, D. V., Brandsma, C.-A., Suzuki, M., Gosselink, J. V., Liu, G., Alekseyev, Y. O., et al. (2012) · Genome Medicine · doi:10.1186/gm368 · Cited by 5
Metadata
- Authors
- Sirota, M., Dudley, J. T., Kim, J., Chiang, A. P., Morgan, A. A., Sweet-Cordero, A., Sage, J., & Butte, A. J.
- Venue
- Science Translational Medicine
- Published
- 2011-08-17
- Volume
- 3
- Issue
- 96
- Pages
- 96ra77-96ra77
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W2137632714
- Cited by
- 799
- References
- 3
- FWCI
- 57.41
- Type
- journal article
- Language
- en
- Open access
- green
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.