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Translational bioinformatics
Translational Bioinformatics (TBI) is an emerging field in the study of health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics, and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients.〔(【引用サイトリンク】url=http://www.amia.org/applications-informatics/translational-bioinformatics )〕 Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application.〔(【引用サイトリンク】title=Translational bioinformatics )〕 Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
==History of Translational Bioinformatics==
Translational bioinformatics is a relatively young field within translational research.〔(【引用サイトリンク】url=http://cctsi.ucdenver.edu/RIIC/Pages/WhatisTranslationalInformatics.aspx )〕 Google trends indicate the use of “bioinformatics” has decreased since the mid 1990s when it was suggested as a transformative approach to biomedical research.〔 It was coined, however, close to ten years earlier. TBI was then presented as means to facilitate data organization, accessibility and improved interpretation of the available biomedical research.〔 It was considered a decision support tool that could integrate biomedical information into decision-making processes that otherwise would have been omitted due to the nature of human memory and thinking patterns.〔
Initially, the focus of TBI was on ontology and vocabulary designs for searching the mass data stores. However, this attempt was largely unsuccessful as preliminary attempts for automation resulted in misinformation. TBI needed to develop a baseline for cross-referencing data with higher order algorithms in order to link data, structures and functions in networks.〔 This went hand in hand with a focus on developing curriculum for graduate level programs and capitalization for funding on the growing public acknowledgement of the potential opportunity in TBI.〔
When the first draft of the human genome was completed in the early 2000s, TBI continued to grow and demonstrate prominence as a means to bridge biological findings with clinical informatics, impacting the opportunities for both industries of biology and healthcare. Expression profiling, text mining for trends analysis, population-based data mining providing biomedical insights, and ontology development has been explored, defined and established as important contributions to TBI.〔 Achievements of the field that have been used for knowledge discovery include linking clinical records to genomics data, linking drugs with ancestry, whole genome sequencing for a group with a common disease, and semantics in literature mining.〔 There has been discussion of cooperative efforts to create cross-jurisdictional strategies for TBI, particularly in Europe. The past decade has also seen the development of personalized medicine and data sharing in pharmacogenomics. These accomplishments have solidified public interest, generated funds for investment in training and further curriculum development, increased demand for skilled personnel in the field and pushed ongoing TBI research and development.〔
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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