The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Open accessAbstract
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and 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
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Network pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622
Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders
Hamosh, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki033 · Cited by 3,420
Drug repositioning: identifying and developing new uses for existing drugs
Ashburn, T. T., & Thor, K. B. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1468 · Cited by 3,237
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,120
Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method
Daniel Gietz, R., & Woods, R. A. (2002) · Methods in Enzymology · doi:10.1016/s0076-6879(02)50957-5 · Cited by 2,871
Relating protein pharmacology by ligand chemistry
Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K. (2007) · Nature Biotechnology · doi:10.1038/nbt1284 · Cited by 2,402
DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs
Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1126 · Cited by 1,831
Cited by
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 892
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
Drug–target interaction prediction: databases, web servers and computational models
Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv066 · Cited by 658
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
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska-Coleska, Z., & Najarian, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz157 · Cited by 429
Metadata
- Authors
- Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., & Tang, Y.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-05-10
- Volume
- 8
- Issue
- 5
- Pages
- e1002503
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W1984084871
- Cited by
- 818
- References
- 53
- FWCI
- 49.89
- Type
- journal article
- Language
- en
- Open access
- gold
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: Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference. https://app.cochat.ai/papers/10.1371/journal.pcbi.1002503
- The paper
- Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., & Tang, Y. (2012). Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1002503
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.