In order to facilitate association studies on genotypic and phenotypic factors related to cervical cancer, ASSIST resorts to medical inferencing applied on real patient data. Following the semantic approach, ASSIST will rely on available standards and recent research achievements in the area of semantics and soft computing in order to build its Medical Knowledge Base.

The targeted virtual unification of the participating archives and interpretation of their content relies upon the semantic indexing of their records. Unlike the conventional way of treating stored medical information as alphanumeric data structures whose interpretation is carried out by the human user, ASSIST’s inference engine will:

• support the virtual unification of the participating archives by translating medical concepts into syntactic values that the legacy systems of the participating archives may perceive and

• undertake the whole process of statistically evaluating medical hypotheses and producing medically important associations based on the collected data.
In addition to the inference engine, ASSIST will incorporate two important interfacing modules:

• The first is the interface to its users, mainly medical researchers and geneticists. This graphical interface will be medical knowledge aware in the sense that it will allow expression of domain specific queries and particular hypotheses by referring to medical ontologies contained in the Medical Knowledge Base.

• The second type includes the interfaces to the participating medical archives and will support exchange of data between them and ASSIST’s core engine and in a way transparent to the end user.

ASSIST will respect and promote the ethical principles that guide current medical research activities and will be designed in full compliance to the legal and ethical national and EU requirements and code of practice. Special care will be taken so as to avoid violation of any form of patient privacy during system operation. To this end, only anonymised patient information will be handled by the ASSIST system, produced by state-of-the art anonymisation techniques and standards.