Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biomedical Informatics Elsevier

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Inc.
ISSN
1532-0464
eISSN
1532-0480
D.O.I.
10.1016/j.jbi.2018.05.003
Publisher site
See Article on Publisher Site

Abstract

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

Journal of Biomedical InformaticsElsevier

Published: Jun 1, 2018

References

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