Mining Knowledge Graph for Drug Discovery
KGC | All Access Subscription
•
22m
2020 Talk: Knowledge Graph for Drug Discovery: A critical barrier in current drug discovery is the inability to utilize public datasets in an integrated fashion to fully understand the actions of drugs and chemical compounds on biological systems. There is a need to intelligently integrate heterogeneous datasets pertaining to compounds, drugs, targets, genes, diseases, and drug side effects now available to enable effective network data mining algorithms to extract important biological relationships. In this talk, we demonstrate the semantic integration of 25 different databases and develop various mining and predication methods to identify hidden associations that could provide valuable directions for further exploration at the experimental level. (Conflict of Interest: I am the co-founder of Data2Discovery - https://www.d2discovery.com/)
Up Next in KGC | All Access Subscription
-
Q&A | Huda Khan, David Kamien and Raf...
Q&A with Huda Khan from Cornell University, David Kamien from Mind Alliances System and Rafael Goncalves from Stanford University.
-
Integrating data from knowledge graph...
As part of the Linked Data For Production: Pathway to Implementation (LD4P2) project’s larger goal of building a pathway to the use of linked data in the description of library resources, we are exploring the integration of linked data sources in library discovery interfaces. Through a series of ...
-
Workshop Summary | The Personal Healt...
Ching-Hua Chen summarizes and talks about the workshop on Personal Health Knowledge Graph.
