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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.


A sub-graph of the knowledge graph. This sub-graph consists of example paths connecting the two concepts C0000545 (Eicosapentaenoic Acid) and C1825292 (FFAR1 gene) which are part of the has target relation.
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Fig4: A sub-graph of the knowledge graph. This sub-graph consists of example paths connecting the two concepts C0000545 (Eicosapentaenoic Acid) and C1825292 (FFAR1 gene) which are part of the has target relation.

Mentions: An example sub-graph of the resulting knowledge graph can be found Figure 4.Figure 4


Discovering relations between indirectly connected biomedical concepts.

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

A sub-graph of the knowledge graph. This sub-graph consists of example paths connecting the two concepts C0000545 (Eicosapentaenoic Acid) and C1825292 (FFAR1 gene) which are part of the has target relation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: A sub-graph of the knowledge graph. This sub-graph consists of example paths connecting the two concepts C0000545 (Eicosapentaenoic Acid) and C1825292 (FFAR1 gene) which are part of the has target relation.
Mentions: An example sub-graph of the resulting knowledge graph can be found Figure 4.Figure 4

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.