The Database of Experimental Biomaterials and their Biological Effect (DEBBIE) was an EU Horizon 2020 funded project to develop an open access database of
biomaterials automatically curated from the scientific literature. DEBBIE
was designed to facilitate a more efficient access to the large literature in the field, generate
a comprehensive map of research activity and findings, and enable evidence-based selection of materials for
Beyond the development of database, the project was dedicated to the creation, adaptation and optimization of
text mining tools for the biomaterials domain. These tools, including the DEBBIE retrieval and annotation
pipeline (see illustration below), are openly available for download and use through the
project’s GitHub repositories.
The Devices, Experimental scaffolds and
Biomaterials Ontology (DEB) is an open resource for organizing information about biomaterials,
their design, manufacture and biological testing. It was developed using text analysis for identifying
ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain’s lexicon.
Topics covered by DEB were validated by members of the biomaterials research community.
DEB may be used
for searching terms, performing annotations for machine learning applications, standardized meta-data indexing
and other cross-disciplinary data exploitation.
If you are a biomaterials scientist, we encourage and welcome your input to this effort! You can flag new terms, definitions and errors through
the 'issues' tab in DEB's GitHub repository.
In addition, below you can find the static and dynamic visualization of the ontology.
An experimental version of DEBBIE is now openly available for browsing. It is still under development,
but we consider it a minimal viability product and are welcoming the feedback and comments of the
The current version of the database contains abstracts automatically retrieved from PubMed,
which were classified as ‘relevant’ to the areas of implants, medical devices, and experimental scaffolds.
The abstracts were then annotated using our novel
data model which includes multiple
lexical resources. The annotations are finally stored a NoSQL database, with each of the annotated concept
labelled by one or more of the following categories:
Associated Biological Process
Effect on Biological System
Manufactured Object Features
Biologically Active Substance
Users can access the information with a simple and highly institutive keyword search through the following link: