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

ABSTRACT: BACKGROUND: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. OBJECTIVE:To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph patte

SUBMITTER: Bakal G 

PROVIDER: S-EPMC6070294 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature