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

Journal of Biomedical Informatics
Gokhan BakalRamakanth Kavuluru

Abstract

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying 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 between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic ...Continue Reading

Citations

May 16, 2020·BMC Bioinformatics·Halil KilicogluDongwook Shin
Dec 29, 2020·Environmental Health Perspectives·Anne E ThessenMelissa A Haendel
Dec 15, 2020·Clinical Physiology and Functional Imaging·Antoine JaminAnne Humeau-Heurtier
Feb 14, 2021·Journal of Biomedical Informatics·Jiebin ChuZhengxing Huang
May 7, 2021·Annual Review of Biomedical Data Science·Tiffany J CallahanLawrence E Hunter
May 28, 2021·Journal of Biomedical Informatics·Abbas Akkasi, Mari-Francine Moens

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