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Novartis Captures the Latest Biological Knowledge for Drug Discovery

 Novartis Captures the Latest Biological Knowledge for Drug Discovery - IoT ONE Case Study
Technology Category
  • Functional Applications - Product Data Management Systems
  • Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
  • Healthcare & Hospitals
Applicable Functions
  • Product Research & Development
Services
  • Data Science Services
  • Software Design & Engineering Services
  • System Integration
The Challenge

Novartis faced the challenge of combining its historical data stores with this burgeoning phenotypic data. They also needed a way to place all this data within the larger context of ongoing medical research from around the world. The Novartis team wanted to combine its data with medical information from NIH’s PubMed. PubMed contains about 25 million abstracts from some 5,600 scientific journals.

The Novartis team sought a way to empower researchers to ask questions connecting the dots between all of this data in the context of the latest medical research.

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 comprises the innovation arm of Novartis, with 6,000 researchers at six locations around the globe.

The Solution

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. Using this capability, the team devised queries to find data linked by the desired node pattern, with a given association strength, and then sort the triangles according to this metric.

Operational Impact
  • [Data Management - Big Data Analysis]

    Empower researchers to correlate experimental data with medical research

  • [Efficiency Improvement - R&D]

    Identify promising compounds to accelerate drug development

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