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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.


ROC curves for the has target and may treat datasets. Figure (a) shows the ROC curves produced based on the validation conducted on the has target dataset. Similarly, Figure (b) shows the ROC curves produced based on the validation conducted on the may treat dataset.
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Fig10: ROC curves for the has target and may treat datasets. Figure (a) shows the ROC curves produced based on the validation conducted on the has target dataset. Similarly, Figure (b) shows the ROC curves produced based on the validation conducted on the may treat dataset.

Mentions: Another interesting finding is that the ROC-curves of the pair-based approach are very steep at the beginning up to a recall level of around 0.6 (see Figure 10). In particular, this can be observed, when using plain features on the has target dataset. This indicates that there are some common path patterns which can be learned by the model and be used to infer the has target relation. Table 4 shows some highly weighted example patterns learned by the model.Figure 10


Discovering relations between indirectly connected biomedical concepts.

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

ROC curves for the has target and may treat datasets. Figure (a) shows the ROC curves produced based on the validation conducted on the has target dataset. Similarly, Figure (b) shows the ROC curves produced based on the validation conducted on the may treat dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig10: ROC curves for the has target and may treat datasets. Figure (a) shows the ROC curves produced based on the validation conducted on the has target dataset. Similarly, Figure (b) shows the ROC curves produced based on the validation conducted on the may treat dataset.
Mentions: Another interesting finding is that the ROC-curves of the pair-based approach are very steep at the beginning up to a recall level of around 0.6 (see Figure 10). In particular, this can be observed, when using plain features on the has target dataset. This indicates that there are some common path patterns which can be learned by the model and be used to infer the has target relation. Table 4 shows some highly weighted example patterns learned by the model.Figure 10

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.