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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature559, 547–555 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature571, 95–98 (2019).

    CAS  Article  Google Scholar 

  3. 3.

    Hakimi, O. et al. The devices, experimental scaffolds, and biomaterials ontology (DEB): a tool for mapping, annotation, and analysis of biomaterials’ data. Adv. Funct. Mater.30, 1909910 (2020).

    CAS  Article  Google Scholar 

  4. 4.

    Hirschman, L.et al. Text mining for the biocuration workflow. Database bas020 (2012).

  5. 5.

    Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J. & Valencia, A. Information retrieval and text mining technologies for chemistry. Chem. Rev.117, 7673–7761 (2017).

    CAS  Article  Google Scholar 

  6. 6.

    Isayev, O. Text mining facilitates materials discovery. Nature571, 42–43 (2019).

    CAS  Article  Google Scholar 

  7. 7.

    Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature521, 436–444 (2015).

    CAS  Article  Google Scholar 

  8. 8.

    Xuan, W. et al. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics35, 1745–1752 (2019).

    Article  Google Scholar 

  9. 9.

    Jha, D. et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat. Commun.10, 5316 (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Tchoua, R. B. et al. A hybrid human-computer approach to the extraction of scientific facts from the literature. Procedia Comput. Sci.80, 386–397 (2016).

    Article  Google Scholar 

Download references

Author information




All authors contributed to the preparation of the manuscript

Corresponding author

Correspondence to Osnat Hakimi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hakimi, O., Krallinger, M. & Ginebra, M. Time to kick-start text mining for biomaterials. Nat Rev Mater 5, 553–556 (2020). https://doi.org/10.1038/s41578-020-0215-z

Download citation


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing