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Risk assessment of rare and complex diseases such as Neuroblastoma requires an efficient management of interdisciplinary data. Recent advances in genomic testing are revealing new diagnosis targets whose storage and analysis is becoming a big challenge. The use of Conceptual Models (CM) defining and structuring Neuroblastoma domain serves as a basis to determine the information required for diagnosing the disease. A Genomic Information System (GeIS) built upon a CM, greatly facilitate the integration and management of the heterogeneous and dispersed genomic data. The correct exploitation of the validated dataset leads to an efficient and early risk assessment for patients with Neuroblastoma.

Effective data management of clinical information is well-known to be a very complex task. The breast cancer context provides a good example of how challenging the problem is in Information Systems terms. In fact, it is one of the basic problems faced by clinicians who work in this area. However, this problem is even bigger when clinicians and biologists try to relate these data to biological data obtained from research studies about this disease. This paper shows how Conceptual Modeling practices provide reliable working environments where clinical and biological information are clearly linked. We focus on miRNAs expression studies in breast cancer patients as a representative study of biological data in this environment. Representing all these biological data using Conceptual Modeling techniques and connecting it with the clinical information properly modeled using the same technique, creates the appropriate background to develop an information system able to manage efficiently and analyze all this information.