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Discovering relations between indirectly connected biomedical concepts.

Weissenborn D, Schroeder M, Tsatsaronis G - J Biomed Semantics (2015)

Bottom Line: Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts.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.Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

View Article: PubMed Central - PubMed

Affiliation: DFKI Projektbüro Berlin, Alt-Moabit 91c, Berlin, 10559 Germany ; Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.

ABSTRACT

Background: The 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.

Results: It 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.

Conclusions: Analysis 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.

No MeSH data available.


Growth of PubMed indexed scientific literature since 1965. The figure plots the number of PubMed indexed articles per year, for the period 1965-2010. The plot shows that the indexed literature grows exponentially (blue line). In parallel, the annotation of the PubMed articles with MeSH terms has so far managed to follow this growth (red line)a.
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Fig1: Growth of PubMed indexed scientific literature since 1965. The figure plots the number of PubMed indexed articles per year, for the period 1965-2010. The plot shows that the indexed literature grows exponentially (blue line). In parallel, the annotation of the PubMed articles with MeSH terms has so far managed to follow this growth (red line)a.

Mentions: An example of how fast the reporting of scientific findings grows in this domain is illustrated in Figure 1, where the number of scientific publications indexed by PubMed is shown to be increasing in an exponential fashion over the past decades. Similar findings can be observed for structured data by examining the growth of a representative database in the biomedical domain, namely the Unified Medical Language System (UMLS), shown in Figure 2.Figure 1


Discovering relations between indirectly connected biomedical concepts.

Weissenborn D, Schroeder M, Tsatsaronis G - J Biomed Semantics (2015)

Growth of PubMed indexed scientific literature since 1965. The figure plots the number of PubMed indexed articles per year, for the period 1965-2010. The plot shows that the indexed literature grows exponentially (blue line). In parallel, the annotation of the PubMed articles with MeSH terms has so far managed to follow this growth (red line)a.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4492092&req=5

Fig1: Growth of PubMed indexed scientific literature since 1965. The figure plots the number of PubMed indexed articles per year, for the period 1965-2010. The plot shows that the indexed literature grows exponentially (blue line). In parallel, the annotation of the PubMed articles with MeSH terms has so far managed to follow this growth (red line)a.
Mentions: An example of how fast the reporting of scientific findings grows in this domain is illustrated in Figure 1, where the number of scientific publications indexed by PubMed is shown to be increasing in an exponential fashion over the past decades. Similar findings can be observed for structured data by examining the growth of a representative database in the biomedical domain, namely the Unified Medical Language System (UMLS), shown in Figure 2.Figure 1

Bottom Line: Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts.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.Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

View Article: PubMed Central - PubMed

Affiliation: DFKI Projektbüro Berlin, Alt-Moabit 91c, Berlin, 10559 Germany ; Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.

ABSTRACT

Background: The 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.

Results: It 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.

Conclusions: Analysis 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.

No MeSH data available.