Our team behind the scenes at SXSW | Building a Disease Specific Knowledge Base


In part two of our SXSW series, our team show how we use AI and machine learning to better understand specific diseases - in this case, Glioblastoma (GBM), the most common and aggressive form of brain cancer.

Given the complexity of a savage disease like GBM, a scientist alone could not read up on all the research that has been done to understand it. At BenevolentAI, we have developed a biomedical knowledge base to contextualise all the relevant information and relationships, over 1 billion of them, across diseases, genes, drugs and all the important research that has already been published.

Our technology platform sits on top of this knowledge base to answer two fundamental questions to help us understand diseases like GBM, i) tell us everything we know about the disease today in a way that is meaningful and ii) show us where the gaps in that knowledge gaps are. 

Our knowledge graph pulls out all the relevant links to ongoing and completed clinical trials, existing compounds that have been tested against the disease, and the underlying biological connections known to be associated with the disease.  

Together, these biological facts represent all that is known to be true about a disease today.
Watch our team demonstrate here how algorithms can analyse this knowledge and reason across it to provide scientists and researchers with capabilities beyond human insight. In this video, we focus on the number of relationships that exist in our knowledge graph linked to GBM.


Up next

Target Identification, Literature Knowledge Inference