Extending XNAT Platform with an Incremental Semantic Framework

Santiago Timón, Mariano Rincón, Rafael Martínez-Tomás

Informatics increases the yield from neuroscience due to improved data. Data sharing and accessibility enable joint efforts between different research groups, as well as replication studies, pivotal for progress in the field. Research data archiving solutions are evolving rapidly to address these necessities, however, distributed data integration is still difficult because of the need of explicit agreements for disparate data models. To address these problems, ontologies are widely used in biomedical research to obtain common vocabularies and logical descriptions, but its application may suffer from scalability issues, domain bias, and loss of low-level data access. With the aim of improving the application of semantic models in biobanking systems, an incremental semantic framework that takes advantage of the latest advances in biomedical ontologies and the XNAT platform is designed and implemented. We follow a layered architecture that allows the alignment of multi-domain biomedical ontologies to manage data at different levels of abstraction. To illustrate this approach, the development is integrated in the JPND (EU Joint Program for Neurodegenerative Disease) APGeM project, focused on finding early biomarkers for Alzheimer's and other dementia related diseases.