Journal of Biomedical Informatics 82 (2018) 189–199 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations b c c a,b, Gokhan Bakal , Preetham Talari , Elijah V. Kakani , Ramakanth Kavuluru Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, United States Department of Computer Science, University of Kentucky, United States Division of Hospital Medicine, Department of Internal Medicine, University of Kentucky, United States ARTIC L E I NF O Keywords: Background: Identifying new potential treatment options for medical conditions that cause human disease Information extraction burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and Relation prediction clinical trials, in vitro approaches are ﬁrst attempted to identify promising candidates. Likewise, identifying Semantic graph patterns diﬀerent causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict speciﬁc relations be- tween any given pair of entities using the distant supervision approach. Objective: To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical
Journal of Biomedical Informatics – Elsevier
Published: Jun 1, 2018
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