ArticleBMC bioinformatics2017
Quantifying and filtering knowledge generated by literature based discovery.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis.bioRxiv : the preprint server for biology · 2026Article
- SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation.BMC bioinformatics · 2025Article
- Is automatic detection of hidden knowledge an anomaly?BMC bioinformatics · 2019Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
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2 authors.
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Abstract
backgroundLiterature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined.
methodsWe present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed.
resultsThe evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance.
conclusionsWhile the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and
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