This is a hybrid event. Join in person at the LCSB, or tune in remotely via Webex.
Causal reasoning and mechanistic modelling using knowledge graphs for drug discovery
In the field of drug discovery, causal reasoning and mechanistic modeling are essential for understanding complex biological systems and identifying potential therapeutic targets. This lecture explores different applications of knowledge graphs, a method that allows for the representation of biomedical knowledge as interconnected networks, into drug discovery.
The lecture presents several causal reasoning algorithms that enable leveraging prior knowledge and omics data for identifying potential drugs for an indication. Furthermore, the lecture outlines how Knowledge Graphs Embedding Models can be used for drug repurposing by training them to predict potential novel associations between drugs and diseases.
About the speaker
Dr Daniel Domingo-Fernández has over 10 years of experience at the intersection of data science and biomedicine. He combines expertise in machine learning, natural language processing, and knowledge graphs with domain expertise in drug discovery, cheminformatics, metabolomics, and experimental biology. Holding an MS and a PhD in Life Science Informatics from the University of Bonn, he previously served as a research fellow at Fraunhofer SCAI. Currently, he manages a remote, inter-continental team of data scientists at Enveda Biosciences.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
