ArticleBioinformatics (Oxford, England)2011
Discovering and visualizing indirect associations between biomedical concepts.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Artificial Intelligence: Applications in Pharmacovigilance Signal Management.Pharmaceutical medicine · 2025Review
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.iScience · 2024Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
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- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules.BMC bioinformatics · 2022Article
- Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects.Frontiers in bioinformatics · 2022Review
- Text mining for identification of biological entities related to antibiotic resistant organisms.PeerJ · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- causalizeR: a text mining algorithm to identify causal relationships in scientific literature.PeerJ · 2021Article
- Women's health inBMJ open · 2020Observational
- KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications.F1000Research · 2020Article
- PPR-SSM: personalized PageRank and semantic similarity measures for entity linking.BMC bioinformatics · 2019Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project.Journal of proteome research · 2018Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
- GWAS for serum galactose-deficient IgA1 implicates critical genes of the O-glycosylation pathway.PLoS genetics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
motivationDiscovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner.
resultsThis article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. AVAILABILITY: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/. CONTACT: tsuruoka@jaist.ac.jp.
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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.