The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Formalizing drug indications on the road to therapeutic intent
Open accessAbstract
Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The state-of-the-art in drug indication curation from text was expanded through InContext, which includes therapeutic context information about drug usage, especially for antineoplastic and cardiovascular drugs.
“The state-ofthe-art in drug indication curation from text was expanded through InContext (49), which includes therapeutic context information about drug usage, especially for antineoplastic and cardiovascular drugs.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [49]
The Previous Efforts and ontology-based formalization sections discuss therapeutic context and text-mining tools, but do not identify InContext or describe its contribution. Searches for InContext, antineoplastic drugs, and cardiovascular drugs found no passage substantiating the attributed claim. (LLM, conf=0.74)
Experimental — accuracy not yet measured
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
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Self-citationThe SIDER database of drugs and side effects
Kuhn, M., Letunic, I., Jensen, L. J., & Bork, P. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1075 · Cited by 1,451
PREDICT: a method for inferring novel drug indications with application to personalized medicine
Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 900
Encyclopedia of Library and Information Sciences
Bates, M. J., & Maack, M. N. (2009) · doi:10.1201/9780203757635 · Cited by 732
Rappaport, N., Twik, M., Plaschkes, I., Nudel, R., Stein, T. I., Levitt, J., Gershoni, M., Morrey, C. P., Safran, M., & Lancet, D. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1012 · Cited by 684
Normalized names for clinical drugs: RxNorm at 6 years
Nelson, S. J., Zeng, K., Kilbourne, J., Powell, T., & Moore, R. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000116 · Cited by 491
Self-citationDrugCentral: online drug compendium
Ursu, O., Holmes, J., Knockel, J., Bologa, C. G., Yang, J. J., Mathias, S. L., Nelson, S. J., & Oprea, T. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw993 · Cited by 323
Self-citationMiller, N., Lacroix, E., & Backus, J. (2000) · PubMed · Cited by 140
Computational and Practical Aspects of Drug Repositioning
Oprea, T. I., & Overington, J. P. (2015) · Assay and Drug Development Technologies · doi:10.1089/adt.2015.29011.tiodrrr · Cited by 114
Self-citationOprea, T. I., Nielsen, S. K., Ursu, O., Yang, J. J., Taboureau, O., Mathias, S. L., Kouskoumvekaki, I. A., Sklar, L. A., & Bologa, C. (2011) · Molecular Informatics · doi:10.1002/minf.201100023 · Cited by 113
Self-citationDevelopment and evaluation of an ensemble resource linking medications to their indications
Wei, W.-Q., Cronin, R. M., Xu, H., Lasko, T. A., Bastarache, L., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001431 · Cited by 106
Building a drug ontology based on RxNorm and other sources
Hanna, J., Joseph, E., Brochhausen, M., et al. (2013) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-4-44 · Cited by 79
Target-Based Drug Repositioning Using Large-Scale Chemical–Protein Interactome Data
Sawada, R., Iwata, H., Mizutani, S., & Yamanishi, Y. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00330 · Cited by 69
Fung, K. W., Jao, C. S., & Demner‐Fushman, D. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001291 · Cited by 63
Pheochromocytoma: a review on preoperative treatment with phenoxybenzamine or doxazosin.
van der Zee, P. A., & de Boer, A. (2014) · PubMed · Cited by 56
Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences.
Carter, J. S., Brown, S. H., Erlbaum, M. S., et al. (2002) · PubMed · Cited by 55
Taylor, R. J., & Bond, C. M. (1991) · PubMed · Cited by 47
LabeledIn: Cataloging labeled indications for human drugs
Khare, R., Li, J., & Lu, Z. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.08.004 · Cited by 43
Prediction of new drug indications based on clinical data and network modularity
Yu, L., Ma, X., Zhang, L., Zhang, J., & Gao, L. (2016) · Scientific Reports · doi:10.1038/srep32530 · Cited by 43
Drug knowledge expressed as computable semantic triples.
Elkin, P. L., Carter, J. S., Nabar, M., et al. (2011) · PubMed · Cited by 13
Flagged first, then by citations
Research works printed but not linked: 1
- Nelson, 2009, The Unified Medical Language System (UMLS) Project, 5320
Cited by
Sorted by citations
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“therapeutic intent computationally is challenging (48).”
Self-citation
Metadata
- Authors
- Nelson, S. J., Oprea, T. I., Ursu, O., Bologa, C. G., Zaveri, A., Holmes, J., Yang, J. J., Mathias, S. L., Mani, S., Tuttle, M. S., & Dumontier, M.
- Affiliations
- University of New Mexico (US)
- Maastricht University (NL)
- University of California, San Francisco (US)
- Corresponding author
- Oprea, Tudor I
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-17
- Volume
- 24
- Issue
- 6
- Pages
- 1169-1172
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC6259666
- OpenAlex
- W2735061578
- Cited by
- 9
- References
- 20
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2018: 2
- FWCI
- 0.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc)
- UNM’s Digital Repository (University of New Mexico) (submittedVersion, cc-by-nc)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (1U54CA189205-01)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- therapeutic intent, drug indications, application ontology, precision medicine, clinical decision support, drug repurposing, sildenafil, dosage strength, medical informatics
- MeSH terms
- Drug Labeling, Drug Therapy, Humans, United States, United States Food and Drug Administration, Vocabulary, Controlled, Precision Medicine, Drug Repositioning
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
