An ontology-based approach for modelling and querying Alzheimer's disease data

Background The recent advances in biotechnology and computer science have led to an ever-increasing avail- ability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained. Methods Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontolo- gies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzhei- mer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner. Results We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI reposi- tory in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease. Conclusions The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multi- variate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD