The rapidly expanding biomaterials data are challenging to organize. Text mining systems are powerful tools that automatically extract and integrate information in large textual collections. As text mining leaps forward by leveraging deep-learning approaches, it is time to address the most pressing biomaterials information and data processing needs.
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Change history
22 March 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41578-021-00307-x
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 751277 and from Plan TL Encargo project of the BSC.
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Related links
BCTEO: bioportal.bioontology.org/ontologies/BCTEO
BioBERT: https://github.com/dmis-lab/biobert
cBiT: cbit.bmt.tue.nl/biomaterial/browse
ChemSpot: informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/chemspot
DEB: https://github.com/ProjectDebbie/Ontology_DEB
DEBBIE: https://github.com/ProjectDebbie
European Materials Modelling Council: https://emmc.eu/
GMDN: gmdnagency.org
MetaMap: metamap.nlm.nih.gov/
NPO: bioportal.bioontology.org/ontologies/NPO
SciBERT: https://github.com/allenai/scibert
SNOMED-CT: snomed.org
TaggerOne: ncbi.nlm.nih.gov/research/bionlp/tools/taggerone/
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Hakimi, O., Krallinger, M. & Ginebra, MP. Time to kick-start text mining for biomaterials. Nat Rev Mater 5, 553–556 (2020). https://doi.org/10.1038/s41578-020-0215-z
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DOI: https://doi.org/10.1038/s41578-020-0215-z
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