An interactive interface for exploring a biomedical knowledge graph with concepts such as drugs, proteins, genes, and diseases. Powered by Wikidata.
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Find similar concepts based on knowledge graph embeddings.
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Wikidata data mining, website
Millions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. Though computational tools can expedite drug candidate discovery, utilizing these tools typically requires programming expertise that many biologists lack. This problem persists due to an ever increasing growth of new biomedical data that is difficult to integrate and maintain; such tools very seldom provide a non-programming interface for researchers to query. This creates an opportunity for a suite of user-friendly software tools to aid computational discovery of new treatments using existing drugs, eliminating the need for researchers to acquire computational expertise in integrating multiple databases and performing algorithmic analysis. Our team has unique biomedical knowledge and software development expertise through our affiliation with the Stanford School of Medicine and Department of Computer Science. Specifically, our aims are to:
The completion of this project will allow researchers direct access to comprehensive biomedical data through intuitive software that aids in decision making with regards to identifying drug candidates to provide faster, more efficacious treatment to all Americans.
Our advisors and collaborators include Sam Piekos, Dan Sosa, Russ Altman, Jaap Suermondt, Erika Strandberg, and Larry Kalesinskas. We thank them for project inspiration and advice.
If you use Biomedical Graph Visualizer in your work, please cite this paper:
Ashton Teng, Blanca Villanueva, Derek Russell Jow, Shih-Cheng Huang, Samantha N Piekos, Russ B Altman bioRxiv 2020.11.27.368811; doi: https://doi.org/10.1101/2020.11.27.368811