Association studies on cervical cancer facilitated by inference and semantic technologies: the assist approach
Mitkas, P.; Koutkias, V.; Symeonidis, A.; Falelakis, M.; Diou, C.; Lekka, I.; Delopoulos, A.; Agorastos, T.; Maglaveras, N.
Studies in Health Technology and Informatics 136: 241-246
2008
ISSN/ISBN: 0926-9630 PMID: 18487738 Document Number: 619451
Cervical cancer (CxCa) is currently the second leading cause of cancer-related deaths, for women between 20 and 39 years old. As infection by the human papillomavirus (HPV) is considered as the central risk factor for CxCa, current research focuses on the role of specific genetic and environmental factors in determining HPV persistence and subsequent progression of the disease. ASSIST is an EU-funded research project that aims to facilitate the design and execution of genetic association studies on CxCa in a systematic way by adopting inference and semantic technologies. Toward this goal, ASSIST provides the means for seamless integration and virtual unification of distributed and heterogeneous CxCa data repositories, and the underlying mechanisms to undertake the entire process of expressing and statistically evaluating medical hypotheses based on the collected data in order to generate medically important associations. The ultimate goal for ASSIST is to foster the biomedical research community by providing an open, integrated and collaborative framework to facilitate genetic association studies.