Knowledge Graphs for Precision Oncology
Knowledge Graphs have in recent years gained a lot of prominence within biomedical AI applications. This partnership holds tremendous potential given the highly complex and sparse nature of biomedical data, along with the need for prior knowledge to be integrated with the world’s knowledge to obtain a deep and comprehensive view of complex disease landscapes such as in Oncology. The unFAIR* nature of this research field makes healthcare AI technically and scientifically challenging, and Knowledge Graphs driven by NLP and GraphML could greatly influence the drug discovery and development processes.
In this talk, I will discuss how our team is applying Knowledge Graphs across the drug discovery pipeline – from target discovery, combination prioritisation, patient stratification and clinical biomarker discovery. Through these projects, I will also discuss how Graphs can help bridge the critical bench-to-bedside gap in biomedical R&D by translating Discovery knowledge to Clinical applications, and vice versa. I will conclude by discussing key bottlenecks and pain-points prevalent in the scientific community that will need to be addressed for Knowledge Graphs to drive and influence key decisions in the drug discovery pipeline.
*FAIR - Findable, Accessible, Interoperable, Reusable
Krishna C. Bulusu
Krishna C. Bulusu is a Director in Oncology Data Science at AstraZeneca and leads the Oncology Knowledge Graphs team. His team’s primary focus is accelerating the impact of data science-driven precision medicine across the drug discovery process – from target discovery, combination prioritisation, to patient stratification and clinical safety. He previously held a Postdoctoral Research Associate position at the Centre for Molecular Informatics in Dept. of Chemistry, Cambridge, predicting safe and efficacious drug combinations for Cancer, Parkinson’s and rare diseases. Krishna is from Hyderabad, India, and received his PhD from the Institute of Cancer Research, London in Computational Biology & Chemogenomics, and holds a Master’s degree in Bioinformatics from the University of Edinburgh.