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


Bakal G. , Talari P., Kakani E., Kavuluru R.

JOURNAL OF BIOMEDICAL INFORMATICS, vol.82, pp.189-199, 2018 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 82
  • Publication Date: 2018
  • Doi Number: 10.1016/j.jbi.2018.05.003
  • Title of Journal : JOURNAL OF BIOMEDICAL INFORMATICS
  • Page Numbers: pp.189-199

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.