University of Pittsburgh

Explaining natural product drug interactions with biomedical knowledge graphs

Graduate Student
Date: 
Friday, April 16, 2021 - 12:30pm - 1:00pm

Use of natural products such as green tea has increased in the US in the past few decades. While these products are not intended to replace conventional medicines, concomitant intake with prescription medicines is common. Natural products, however, can interact with conventional medicines and lead to adverse events in certain cases. While there is a plethora of research on drug-drug interactions for conventional drugs, similar attention is not given to natural product-drug interactions (NPDIs). With the growing use of natural products, it is important to understand the mechanisms underlying their interactions with other chemical substances as well as address safety concerns related to the use of natural products to prevent adverse interactions. Biomedical knowledge graphs can be effectively utilized in this case to investigate biological mechanisms using existing curated knowledge. This talk focuses on developing a heterogeneous knowledge graph with biomedical data sources and machine reading to find mechanistic explanations of NPDIs. It further explores the idea of using graph representation learning or embeddings for knowledge graph completion and inference. We show the utility of knowledge graphs for this task with case studies of natural products and share preliminary results and future directions.

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