Causal knowledge graph analysis identifies adverse drug effects Drug mechanisms Year: 2025 Venue: Bioinformatics Authors: Sumyyah Toonsi, Paul N Schofield, Robert Hoehndorf DOI: 10.1093/bioinformatics/btaf661 Abstract The data is available through https://github.com/bio-ontology-research-group/Mediation-Analysis-using-Causal-Knowledge-Graph. Topics Drug mechanisms
Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications Drug mechanisms Neuro-Symbolic AI Year: 2022 Venue: PeerJ Authors: Mona Alshahrani, Abdullah Almansour, Asma Alkhaldi, Maha A. Thafar, Mahmut Uludag, Magbubah Essack, Robert Hoehndorf DOI: 10.7717/peerj.13061 Abstract Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug
DDIEM: drug database for inborn errors of metabolism Drug mechanisms Rare disease Year: 2020 Venue: Orphanet Journal of Rare Diseases Authors: Marwa Abdelhakim, Eunice McMurray, Ali Raza Syed, Senay Kafkas, Allan Anthony Kamau, Paul N Schofield, Robert Hoehndorf DOI: 10.1186/s13023-020-01428-2 Abstract Abstract Background Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of
