The multidisciplinary team, consisting of Osnat Hakimi, Martin Krallinger and Maria Pau Ginebra, proposes to use data mining text technologies to extract information about biomaterials, which is currently dispersed across scientific articles, patents, FDA reports and congress proceedings.
These methods of advanced data mining, together with deep learning techniques, could reveal associations not previously 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, and are highly used in modern medicine and surgery (implants, prostheses, etc.). Their design involves tapping into complex processes, such as the interactions between cells and materials and the degradation of materials in the body.
The rising volume of published results in the field is contrasted by a low degree of sharing and systematization of data. The article explains the specific challenges in the highly multidisciplinary domain of biomaterials, and proposes steps to tackle them and 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 and dedicated to the development of the first biomaterial database using data mining tools. The project is hosted by the UPC and the BSC and can be viewed online .
The paper titled " Time to kick- start text mining for Biomaterials " is authored by Osnat Hakimi, Martin Krallinger and Maria-Pau Ginebra.