ArticleJournal of biomedical informatics2015
Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
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- The Design and Synthesis of a New Series of 1,2,3-Triazole-Cored Structures Tethering Aryl Urea and Their Highly Selective Cytotoxicity toward HepG2.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Nanoformulation improves antitumor efficacy of MAOI immune checkpoint blockade therapy without causing aggression-related side effects.Frontiers in pharmacology · 2022Article
- Article
- A Drug-Side Effect Context-Sensitive Network approach for drug target prediction.Bioinformatics (Oxford, England) · 2019Article
- Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis.JAMIA open · 2019Article
- State of the art of overcoming efflux transporter mediated multidrug resistance of breast cancer.Translational cancer research · 2019Review
- Danshen extract circumvents drug resistance and represses cell growth in human oral cancer cells.BMC complementary and alternative medicine · 2017Article
- tcTKB: an integrated cardiovascular toxicity knowledge base for targeted cancer drugs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.