Summary
Objectives
Biomedical Informatics as a whole faces a difficult epistemological task, since there
is no foundation to explain the complexities of modeling clinical medicine and the
many relationships between genotype, phenotype, and environment. This paper discusses
current efforts to investigate such relationships, intended to lead to better diagnostic
and therapeutic procedures and the development of treatments that could make personalized
medicine a reality.
Methods
To achieve this goal there are a number of issues to overcome. Primary are the rapidly
growing numbers of heterogeneous data sources which must be integrated to support
personalized medicine. Solutions involving the use of domain driven information models
of heterogeneous data sources are described in conjunction with controlled ontologies
and terminologies. A number of such applications are discussed.
Results
Researchers have realized that many dimensions of biology and medicine aim to understand
and model the informational mechanisms that support more precise clinical diagnostic,
prognostic and therapeutic procedures. As long as data grows exponentially, novel
Biomedical Informatics approaches and tools are needed to manage the data. Although
researchers are typically able to manage this information within specific, usually
narrow contexts of clinical investigation, novel approaches for both training and
clinical usage must be developed.
Conclusion
After some preliminary overoptimistic expectations, it seems clear now that genetics
alone cannot transform medicine. In order to achieve this, heterogeneous clinical
and genomic data source must be integrated in scientifically meaningful and productive
systems. This will include hypothesis-driven scientific research systems along with
well understood information systems to support such research. These in turn will enable
the faster advancement of personalized medicine.
Keywords
Personalized medicine - genomic information models
