Elsevier, a leader in global information analytics, has launched EmBiology, a research tool that visualises a comprehensive landscape of biological relationships, empowering researchers to gain a rapid understanding of disease biology and focus on critical evidence.
Key highlights:
- Elsevier has launched its EmBiology tool that allows researchers to gain a rapid understanding of disease biology based on critical evidence.
- EmBiology draws on a broad and deep database of multiple sources, including journal articles, third-party publishers, abstracts and clinical trials.
- Elsevier automates data curation with customised Machine Learning (ML) technology that transforms unstructured text into structured information.
- Results are displayed in a Sankey diagram that visually facilitates understanding of disease development, progression, and drug responsiveness, whatever the researchers’ level of data skills.
Powered by Elsevier’s Biology Knowledge Graph, EmBiology draws on a broad and deep database of multiple sources, including over 7.2 million full text journal articles from high impact Elsevier and third-party publishers, 34.5 million abstracts and 430,000 clinical trials.
Researchers working in drug discovery and development will be able to intuitively explore biological relationships and concepts to improve drug target and biomarker identification and prioritisation. The depth and reliability of EmBiology’s data will enable more confident decision making about what targets to pursue and how to modulate the disease process.
Dr. Sherry Winter, director of biology and biomedical solutions at Elsevier, commented:
Researchers at pharmaceutical companies are faced with the monumental task to uncover insights from overwhelming amounts of information in order to identify new, more effective therapies.
"Intense competition demands that insights are identified quickly and confidently; researchers need to be equipped with data skills to do that.
“EmBiology relieves this pressure by collating critical disease data into a single place and structuring it in an interactive way. This facilitates evidence-based research decisions for successful projects that ultimately bring novel treatments to patients faster.”
EmBiology surfaces relationship information, including directionality and effect, for a wide range of subject areas including expression, biomarkers, and regulation. Elsevier automates data curation with customised Machine Learning (ML) technology that transforms unstructured text into structured information.
The resulting knowledge graph includes 1.4 million entities connected by 15.7 million relationships to enable deep insights from a broad range of literature, with more added weekly, so researchers can be confident they are always viewing the latest concepts and terms.
Results are displayed in a Sankey diagram that visually facilitates understanding of disease development, progression, and drug responsiveness, whatever the researchers’ level of data skills. Users can also apply filters to narrow down their research question and rapidly confirm their experimental hypotheses for new drug targets, biomarkers, and drug repurposing projects.
“Elsevier has a longstanding reputation as a leader in managing and disseminating scientific information and classifying life science data. Our Biology Knowledge Graph has been developed over a period spanning more than 15 years by a team of PhD-level subject matter experts, so the insights it delivers are unparalleled. With EmBiology, we’ve taken care of the technical data side of R&D, so researchers can focus on the science,” continued Dr. Winter.