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EmBiology provides fast access to biological relationships and pathways
Visualize millions of biological relationships to understand disease biology and get targeted data using publication, relation and concept filters. EmBiology helps you minimize the risk of research bias by removing the reliance on known search terms.
Deep biological evidence to support critical insights and decisions
Transform biological discovery for deeper and more relevant results
EmBiology connects an unparalleled breadth of biological evidence via an AI-driven knowledge graph. Visualization tools, such as a Sankey diagram, and extensive filters on top of that data help you:
Easily find cause-and-effect relationships in experimental results
Minimize the risk of research bias by removing the reliance on known search terms
Better understand disease biology
Improve target and/or biomarker identification and prioritization
Decide what drug targets to pursue and how to measure them
Make evidence-based research decisions to bring effective therapies to market faster
Pharmaceutical companies face intense competition to be first to market. EmBiology facilitates insights and informed decision making.
Gain a comprehensive view of the landscape, including novel results
Get the targeted biological relationship data you need from vast amounts of research, including high-impact journals
Draw on more reliable reported relationships published in multiple sources
Find data updated weekly extracted from full-text content
Biology Knowledge Graph
Watch a short video opens in new tab/window introduction to the Biology Knowledge Graph, the dataset that powers EmBiology. Biology Knowledge Graph data can be searched via EmBiology or licensed off-platform for applied analytics and modeling.
EmBiology product features
Data structure
1.4 million entities connected by 15.7 million relationships (with more added weekly), plus 87.2 million referenced and viewable facts.
Comprehensive biological content
Data is extracted from a rich array of sources, including the full text of articles. Wide journal coverage from Elsevier and non-Elsevier sources.
Natural language processing technology
TERMite text analysis engine for entity recognition plus machine learning to recognize relationships and associated properties.
Quality control of biological data
Expert reviewers have deep backgrounds and experience. They follow stringent QC processes and data are held to the highest standards.
Accurate and up to date
New full-text articles are scanned weekly. The entire dataset is rescanned annually and updated with new concepts and terms.
“Using EmBiology could result in a 20-25% global improvement on rate of success.”
