ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.Molecules (Basel, Switzerland) · 2022Article
- Exploration of Shared Themes Between Food Security and Internet of Things Research Through Literature-Based Discovery.Frontiers in research metrics and analytics · 2021Article
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- Article
- Indirect association and ranking hypotheses for literature based discovery.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Literature Based discovery (LBD) seeks to find information implicit in text, but never explicitly stated. In this work, we develop a method of visually summarizing LBD output in an automatically generated tree structure. This structure promotes a comprehensive understanding of LBD output as a whole, and encourages the user to explore branches of the hierarchy they find most interesting or surprising. This novel visualization system requires the development and integration of automatic functional group discovery, set associations, and linking set associations. Specifically, we perform hierarchical clustering on the potential discoveries generated by an LBD system to create a tree of potential hypotheses. We weight the tree by developing set association measures, and extending them to linking set association measures. This weighted tree is displayed in an interactive visual environment, and validated by replicating the historic Raynaud's Disease - fish oil discovery.
Identifiers
31259013PMC6568119What 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.