- 1. Identifying disease trajectories with predicate information from a knowledge graph
- (Publication)
- Created on 11 December 2020
- 2. Using predicate information from a knowledge graph to identify disease trajectories
- (Publication)
- Created on 11 December 2020
- 3. Drug prioritization using the semantic properties of a knowledge graph
- (Publication)
- Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be ...
- Created on 11 December 2020
- 4. Using predicate and provenance information from a knowledge graph for drug efficacy screening
- (Publication)
- © 2018 The Author(s). Background: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object ...
- Created on 11 December 2020
- 5. Automated extraction of potential migraine biomarkers using a semantic graph
- (Publication)
- © 2017 The Author(s) Problem Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and ...
- Created on 11 December 2020
- 6. RELigator: Chemical-disease relation extraction using prior knowledge and textual information
- (Publication)
- The Erasmus MC team participated in the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtask: automatic disease named entity recognition and normalization ...
- Created on 16 November 2016