Extending XNAT Platform with an Incremental Semantic Framework - Timón, Santiago; Rincón, Mariano; Martínez-Tomás, Rafael (Frontiers in Neuroinformatics, 2017)
Informatics increases the yield from neuroscience due to improved data analysis and machine learning algorithms. Data sharing and data 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 coordination and integration is still difficult because of the need of explicit agreements and/or transformations for disparate data models. To tackle these situations, ontologies are widely used in biomedical research to obtain common vocabularies and logical descriptions for the data but its application may suffer from flaws like scalability issues, domain bias and lose 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 bio-medical ontologies and the XNAT platform is designed and implemented. We follow a layered architecture that shifts XNAT semantics, allowing the alignment of multi-domain Biomedical ontologies to use sound vocabularies and knowledge models and to manage data at different levels of abstraction to maintain full control of the data in the same environment. To validate 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.