If you work in systems biology, you’re tasked with understanding the connections between genes, proteins, cells and tissues. If you work in chemistry, each individual molecule renders its own graph. Even healthcare organizations must map patient journeys to better understand disease progression or prevent poor outcomes. In each of these cases, you’re solving problems naturally represented by interconnected data.
That’s why life sciences users – pharmaceutical companies, chemical manufacturers, agriculture companies, biotech startups and healthcare providers – are leveraging Neo4j to analyze their connected data in ways not possible without graphs. Neo4j, a native graph database specifically designed to store and process your connected data, helps solve complicated life sciences problems at every scale.
Life science companies – dealing with everything from patients to molecules – understand the value of graphs for R&D, privacy and regulatory compliance, medical equipment manufacturing and affiliation management between healthcare providers (HCPs), patients and organizations. Neo4j has enabled companies like Novartis and ChemAxon to extract novel insights about relationships between biological and chemical data to accelerate drug discovery. We’ve also empowered companies like Monsanto to track genetic relationships in corn to breed better crops and feed the world’s growing population.
Gartner Research: COVID-19 Demands Urgent Use of Graph Data Management and Analytics
Learn how graph technology and data science can be used in healthcare to analyze epidemiological data, identify outbreak recurrence and flatten the COVID-19 curve.Read the Gartner Report
NYP Advances Analysis to Track Infections with Neo4j
Learn how NYP Hospital uses graph databases to relate all their event data, enabling them to track infections and take strategic action to contain them.Read the Case Study
Integrating All of Biology into a Public Neo4j Database
Discover how the Hetionet knowledge graph uses Neo4j to identify new uses for existing drugs, and genetic targets used to develop new ones.Read the Blog Post
Building a Life Sciences Knowledge Graph from Scratch
Learn about building a cancer drug discovery knowledge graph using tools to capture, connect, store, query and visualize a landscape of biotech/pharma companies.Watch the Video
Learn how the Novartis team uses Neo4j to mine huge volumes of biological data to support the development of next-gen medicines.Read the Case Study
Life sciences researcher studies large datasets and uncovers potential new insights with the power of Neo4j.Read the Article