Transitional bioinformatics is a relatively new field in biomedical informatics that is concerned with the application of clinical data dat and findings to biological and clinical problems. As the pool of clinical, biological and genetic data increases, so must new methods and procedures for storing, retrieving and applying this data into applicable uses. Without the "translation" of this data into an informational state that allows the data to be applied to clinical and biological problems, any information that can be derived from collected information would remain inaccessible. This field has emerged with both the advent of computer systems that can store and retrieve large amounts of data as well as the ability to share data between researchers and clinicians (Butte, 2008). This field specifically refers to the application of large amounts of collective data to clinical problems.
The field of translational bioinformatics is a relatively new field. In response to the expanding acquisition of stored clinical data, this pool of data has increased to such a size that new methods were needed to manage and mine this data. The mapping of the human genome, in particular, presented a grand opportunity to discover treatments for several human diseases, if only the data could be converted in a form that was manageable by computer algorithms. In 1999, the Working Group on Biomedical Computing Advisory Committee made a recommendation to the Director National Institutes of Health, pointing out that the fields of computing and biomedicine had both been growing rapidly and new methods to apply this data to clinical and biological problems were needed. This report made four recommendations, which included establishing methods to manage biological data and provide incentives and resources to build up the computer infrastructure that was needed to accomplish these tasks ("The biomedical information," 1999). Shortly afterwards, the term "translational bioinformatics" began to appear in the literature, with one of the first mentioned this term appearing in a article by Butte in 2006.
Examples and Applications
The traditional way of storing data presented many challenges for informatics. The first challenge was that clinical data was traditionally stored in "free text", where clinical concepts would need to either need to be translated into coded data by an analyst or by a natural language processor. Because free text is often rife with subtleties and contextual information, a human interpreter is needed to code the data to assure that stored information is accurate to the original findings. Coding the data presents is own challenges and is a vital part of the process. Normalization is established by using a unified terminology such as Unified Medical Language System (UMLS) and MeSH terms for document searches. This "translation" from clinical findings to normalized information is a vital step to be able to use the data for computerized searches and research. Once data has been normalized, it can be shared with other researchers, who can help increase the clinical body of knowledge through discovery and collaboration. Most of the most successful examples of translational biomedical informatics work include genetic databases such as GenBank (created in 1980), the Gene Expression Omnibus, as well as ArrayExpress, which provides samples from over 3,000 samples (Butte, 2008). These efforts have greatly increased the amount of data available for experiments. In addition to making the data globally available and searchable, different findings that use the data can be easily compared and contrasted.
Butte, A., & Chen, R. (2006). Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics. AMIA 2006 symposium proceedings. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839582/pdf/AMIA2006_0106.pdf
Butte, A. J. (2008). Translational bioinformatics: Coming of age. Journal of the American Medical Informatics Association, 15(6), 709-714. Retrieved from http://jamia.bmj.com/content/15/6/709.full.pdf
Indrea, N., Butte, A., Lussier, Y., Tarczy-Hornoch, P., & Ohno-Machado, L. (2011, May). Translational bioinformatics: linking knowledge across biological and clinical realms. Journal of the American Medical Informatics Association, 18, 354-357. Retrieved from http://jamia.bmj.com/content/18/4/354.full.pdf
Working Group on Biomedical Computing Advisory Committee to the Director National Institutes of Health, (1999). The biomedical information science and technology initiative. Retrieved from website: http://www.bisti.nih.gov/library/june_1999_Rpt.asp
Submitted by Kimberley Anne Gray