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Novartis Leverages IoT for Enhanced Drug Discovery
Technology Category
- Analytics & Modeling - Computer Vision Software
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Healthcare & Hospitals
- Mining
Applicable Functions
- Product Research & Development
Use Cases
- Clinical Image Analysis
- Traffic Monitoring
The Challenge
Novartis, a global healthcare company, was faced with the challenge of managing and making sense of a vast amount of data. The company had decades of data on how various compounds affect protein targets, with about a billion data points in total. This historical data was critical but sparse compared to the granular data currently being collected. Novartis uses an automated process that captures high-content image data showing how a particular compound has affected an entire cell culture, generating terabytes of phenotypic data. The challenge was to combine this historical data with the burgeoning phenotypic data and place it within the larger context of ongoing medical research from around the world. The team also wanted to combine its data with medical information from NIH’s PubMed, which contains about 25 million abstracts from some 5,600 scientific journals.
The Customer
Novartis
About The Customer
Novartis is a global healthcare company based in Basel, Switzerland that provides solutions to address the evolving needs of patients. It is one of the largest pharmaceutical companies by both market capitalization and sales. The Novartis Institutes for BioMedical Research comprise the innovation arm of Novartis, with 6,000 researchers at six locations around the globe. The company has amassed decades of data on how various compounds affect protein targets, and uses an automated process that captures high-content image data showing how a particular compound has affected an entire cell culture.
The Solution
Novartis decided to create a knowledge graph stored in Neo4j, and devised a processing pipeline for ingesting the latest medical research. Text mining is used at the beginning of the pipeline to extract relevant text data from PubMed. That data is then fed into Neo4j, along with Novartis’s own historical and image data. The data pipeline populates the 15 kinds of nodes that were devised to encode the data. The next phase fills in the relationship information that links the nodes together. The team identified more than 90 different relationships. Novartis uses Neo4j graph algorithms to traverse the graph and identify a desired triangular node pattern linking the three classes of data together. Graph analytics not only find relevant nodes in the desired triangular relationship, but also employ a metric the team designed to gauge the associated strength between each node in each triangle.
Operational Impact
Quantitative Benefit
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