ArticleJournal of biomedical discovery and collaboration2010
EpiphaNet: An Interactive Tool to Support Biomedical Discoveries.
Article in Journal of biomedical discovery and collaboration, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed.
- Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?Medical research archives · 2024Article
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Avoiding background knowledge: literature based discovery from important information.BMC bioinformatics · 2023Article
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.Journal of biomedical informatics · 2021Article
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- Exploration of Shared Themes Between Food Security and Internet of Things Research Through Literature-Based Discovery.Frontiers in research metrics and analytics · 2021Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Structured reviews for data and knowledge-driven research.Database : the journal of biological databases and curation · 2020Article
- Indirect association and ranking hypotheses for literature based discovery.BMC bioinformatics · 2019Article
- A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- Investigating the role of interleukin-1 beta and glutamate in inflammatory bowel disease and epilepsy using discovery browsing.Journal of biomedical semantics · 2018Article
- Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery.Journal of data and information science (Warsaw, Poland) · 2017Article
- Using Pathfinder networks to discover alignment between expert and consumer conceptual knowledge from online vaccine content.Journal of biomedical informatics · 2017Article
- Informatics Support for Basic Research in Biomedicine.ILAR journal · 2017Review
- Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation.Journal of biomedical discovery and collaboration · 2016Article
- Literature-Based Discovery of Confounding in Observational Clinical Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Reasoning with Vectors: A Continuous Model for Fast Robust Inference.Logic journal of the IGPL · 2015Article
- Systematic review automation technologies.Systematic reviews · 2014Article
- A graph-based recovery and decomposition of Swanson's hypothesis using semantic predications.Journal of biomedical informatics · 2013Article
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
No grant is acknowledged in the PubMed record.
Abstract
unlabelledBackground. EpiphaNet is an interactive knowledge discovery system which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature-based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists.
resultsThe system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned.
conclusionsEpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them.
Identifiers
20859853PMC2990276What OpenQuestion holds
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.