Discovering relations between indirectly connected biomedical concepts
Discovering relations between indirectly connected biomedical concepts
复制标题
发现间接关联的生物医学概念之间的关系
DOI：
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发表时间：
2014
影响因子：
1.9
通讯作者：
G. Tsatsaronis
中科院分区：
文献类型：
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作者：
Dirk Weissenborn;M. Schroeder;G. Tsatsaronis
生物医学领域知识的复杂性和规模促使人们从结构化和非结构化知识库中挖掘异构数据。朝着这个方向，有必要联合收割机的事实，以制定假设或得出结论的领域概念。这项工作解决了这个问题，通过使用间接知识连接知识图中的两个概念，发现它们之间的隐藏关系。图将概念表示为顶点，将关系表示为边，这些概念源自结构化（本体）和非结构化（文本）数据。在这个图中，路径模式，即序列的关系，挖掘使用远程监督，潜在的生物医学关系的特征。使用机器学习可以从这种表示中识别生物医学关系的特征路径模式。对于实验评价，选择两个频繁的生物医学关系，即“具有靶点”和“可治疗”。结果表明，使用间接知识的关系发现是可能的，AUC可以达到0.8，与随机分类相比，这是一个很大的改进，并且表明可以通过遵循建议的方法优先考虑良好的预测。结果分析表明，模型可以成功地学习表达路径模式的检查关系。此外，这项工作表明，构建的图允许异构信息的轻松集成和生物医学概念之间的间接连接的发现。
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. 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. 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.
DOI：
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发表时间：
1999-08
期刊：
Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子：
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作者：
M. Craven;J. Kumlien
通讯作者：
M. Craven;J. Kumlien