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AstraZeneca, which is headquartered in Cambridge, UK, has a broad portfolio of prescription medicines, primarily for the treatment of diseases in Oncology; Cardiovascular, Renal & Metabolism; and Respiratory & Immunology.

 

“…The vast amount of data our research scientists have access to is exponentially growing each year and maintaining a comprehensive knowledge of all this information is increasingly challenging, ” Gavin Edwards, a Machine Learning Engineer at AstraZeneca wrote.

 

Edwards is part of AstraZeneca’s Biological Insights Knowledge Graph (BIKG) team. He explains that knowledge graphs are networks of contextualized scientific data such as genes, proteins, diseases, and compounds—and the relationship between them. 

 

As these knowledge graphs grow and become more complex, machine learning gives AstraZeneca’s BIKG team a way to analyze the data within them and find relevant connections more quickly and efficiently.

 

 “We can use this approach to identify, say, the top 10 drug targets our scientists should pursue for a given disease,” Edwards wrote.

 

Since a great deal of the data used to form knowledge graphs comes in the form of unstructured text, AstraZeneca uses PyTorch’s library of natural language processing (NLP) to define and train models. They use Microsoft’s Azure Machine Learning platform in conjunction with PyTorch to create machine learning models for recommending drug targets.

 

Learn more about how AstraZeneca is using Microsoft Azure and PyTorch in an effort to accelerate drug discovery.

Brought to you by Dr. Ware, Microsoft Office 365 Silver Partner, Charleston SC.

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