Time to kick-start text mining for biomaterials

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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All authors contributed to the preparation of the manuscript

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Correspondence to Osnat Hakimi.

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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

GO: http://geneontology.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/

UMLS: https://uts.nlm.nih.gov/home.html

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Hakimi, O., Krallinger, M. & Ginebra, M. Time to kick-start text mining for biomaterials. Nat Rev Mater (2020). https://doi.org/10.1038/s41578-020-0215-z

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