ArticleJournal of biomedical semantics2015
Discovering relations between indirectly connected biomedical concepts.
Article in Journal of biomedical semantics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile.Journal of biomedical semantics · 2020Article
- Using predicate and provenance information from a knowledge graph for drug efficacy screening.Journal of biomedical semantics · 2018Article
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Authors and funding
3 authors.
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Abstract
backgroundThe complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation.
resultsIt is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely "has target", and "may treat", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach.
conclusionsAnalysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.
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