Debbie_img

About

The Database of Experimental Biomaterials and their Biological Effect (DEBBIE) is an open access database of biomaterials, implants and medical devices 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 medical applications.

Beyond the development of database, the project is 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.


Pipeline

Ontology

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.


static

Static Visualization on OWLGrEd

dynamic

Dynamic Visualization on WebVOWL

Database

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 research community.

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:

Users can access the information with a simple and highly institutive keyword search through the following link:




Research

Team

Osnat

Osnat Hakimi

Project Leader

Osnat is a Marie Curie Individual Fellow at the Universitat Politècnica de Catalunya and a visitor to the Barcelona Supercomputing Center

austin

Austin McKitrick

Research Assistant

Austin is a Masters Student in Bioinformatics for Health Sciences at Universitat Pompeu Fabra (UPF) in Barcelona

carla

Carla V. Fuenteslópez

Collaborator

Carla is a DPhil Student studying Engineering Science at the University of Oxford

javi

Javier Corvi

Collaborator

Javi is a Research Engineer at the Barcelona Supercomputing Center

Scientific Advisors

osnat

Maria-Pau Ginebra

Maria-Pau leads the Department of Materials Science and Engineering at the Universitat Politècnica de Catalunya

austin

Martin Krallinger

Martin leads the Text Mining Unit at the Barcelona Supercomputing Center

carla

Josep Gelpi

Josep leads the Computational Team for the National Institute of Bioinformatics at the Barcelona Supercomputing Center

javi

Alexandre Perera Lluna

Alexandre leads the Bioinformatics and Biomedical Signals Laboratory (B2S) at the Universitat Politècnica de Catalunya