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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
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).
Hirschman, L.et al. Text mining for the biocuration workflow. Database bas020 (2012).
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J. & Valencia, A. Information retrieval and text mining technologies for chemistry. Chem. Rev. 117, 7673–7761 (2017).
Isayev, O. Text mining facilitates materials discovery. Nature 571, 42–43 (2019).
Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Xuan, W. et al. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 35, 1745–1752 (2019).
Jha, D. et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat. Commun. 10, 5316 (2019).
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).
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.
The authors declare no competing interests.
European Materials Modelling Council: https://emmc.eu/
About this article
Cite this article
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
International Journal of Energy Research (2021)
MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature
Computational Materials Science (2021)