A team of bioengineering and computing experts outlines how the creation of text mining and artificial intelligence tools can advance biomaterials research and development
The application of text mining technologies will increase the ability to extract information from the growing biomaterials literature, and deep learning tools will help to identify implicit links between substantiated information, as well as make predictions and recommendations. This comment article by authors from the BSC-CNS, the UPC and the IBEC has been published today in 'Nature Review Materials' magazine.
Jun 19, 2020
On 19 June 2020, Nature Reviews Materials published an article signed by scientists from the Barcelona Supercomputing Center (BSC) , the Universitat Politècnica de Catalunya · BarcelonaTech (UPC) and the Institute for Bioengineering of Catalonia (IBEC) outlining the great possibilities that artificial intelligence offers for the advancement of biomaterials design and development.
The multidisciplinary team consisting of Osnat Hakimi from Department of Materials Science and Engineering (CEM) , Martin Krallinger, leading researcher Life Sciences - Text Mining del BSC-CNS, and Maria Pau Ginebra proposes using data mining text technologies to extract information about biomaterials that is currently dispersed across scientific articles, patents, FDA reports and conference proceedings.
These methods of advanced data mining, together with deep learning techniques, could reveal associations that have not previously been considered between materials’ attributes and biological responses, and could help with the design and discovery of new biomaterials. Biomaterials are materials that interact with biological systems. They are used intensively in modern medicine and surgery (implants, prostheses, etc.) and designing them involves tapping into complex processes, such as the interactions between cells and materials and the degradation of materials in the body.
Increasing amounts of published results in the field are coupled with a low degree of sharing and systematisation of data. The article explains specific challenges in the highly multidisciplinary domain of biomaterials and proposes steps to tackle them and to enable the organisation and exploitation of accumulated data.
This article has been written in the context of the DEBBIE project, a Marie Skłodowska-Curie action funded by the European Commission that is devoted to the development of the first biomaterials database using data mining tools. The project is hosted by the UPC and the BSC.
