Summary
Objectives To review the issues that have arisen with the advent of translational research in
terms of integration of data and knowledge, and survey current efforts to address
these issues.
MethodsUsing examples form the biomedical literature, we identified new trends in biomedical
research and their impact on bioinformatics. We analyzed the requirements for effective
knowledge repositories and studied issues in the integration of biomedical knowledge.
Results New diagnostic and therapeutic approaches based on gene expression patterns have
brought about new issues in the statistical analysis of data, and new workflows are
needed are needed to support translational research. Interoperable data repositories
based on standard annotations, infrastructures and services are needed to support
the pooling and meta-analysis of data, as well as their comparison to earlier experiments.
High-quality, integrated ontologies and knowledge bases serve as a source of prior
knowledge used in combination with traditional data mining techniques and contribute
to the development of more effective data analysis strategies.
Conclusion As biomedical research evolves from traditional clinical and biological investigations
towards omics sciences and translational research, specific needs have emerged, including
integrating data collected in research studies with patient clinical data, linking
omics knowledge with medical knowledge, modeling the molecular basis of diseases,
and developing tools that support in-depth analysis of research data. As such, translational
research illustrates the need to bridge the gap between bioinformatics and medical
informatics, and opens new avenues for biomedical informatics research.
Keywords
Medical Informatics - bioinformatics - databases - distributed knowledge bases