ASSIST: Employing Inference and Semantic Technologies to Facilitate Association Studies on Cervical Cancer

Advances in biomedical engineering have lately facilitated medical data acquisition, leading to increased availability of both genetic and phenotypic patient. Particularly, in the area of cervical cancer intensive research investigates the role of specific genetic and environmental factors in determining the persistence of the HPV virus – which is the primary causal factor of cervical cancer – and the subsequent progression of the disease. To this direction, genetic association studies constitute a widely used scientific approach for medical research. However, despite the increased data availability worldwide, individual studies are often inconclusive due to the physical and conceptual isolation of the medical centers that limit the pool of data actually available to each researcher. ASSIST, an EU-funded research project, aims at facilitating medical research on cervical cancer by tackling these data isolation issues. To accomplish that, it virtually unifies multiple patient record repositories, physically located at different sites and subsequently employs inferencing techniques on the unified medical knowledge to enable the execution of cervical cancer related association studies that comprise both genotypic and phenotypic study factors, allowing medical researchers to perform more complex and reliable association studies on larger, high-quality datasets.