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.
[2] TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application.
[3] Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
[7] TBI was then presented as means to facilitate data organization, accessibility and improved interpretation of the available biomedical research.
[6][8] 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.
[8] The opportunity for application of TBI is much broader as increasingly medical journals are mentioning the term "informatics" and discussing bioinformatics related topics.
[9] There are increasing numbers of conferences and forums focused on TBI to create opportunities for knowledge sharing and field development.
General topics that appear in recent conferences include: (1) personal genomics and genomic infrastructure, (2) drug and gene research for adverse events, interactions and repurposing of drugs, (3) biomarkers and phenotype representation, (4) sequencing, science and systems medicine, (5) computational and analytical methodologies for TBI, and (6) application of bridging genetic research and clinical practice.
Translational bioinformatics is therefore transforming the search for disease genes and is becoming a crucial component of other areas of medical research including pharmacogenomics.
For instance, the GenBank database, funded by the National Institute of Health (NIH), currently holds 82 billion nucleotides in 78 million sequences coding for 270,000 species.
Data integration serves to utilize the wealth of information available in bioinformatics to improve patient health and safety.
DSS used in this regard identify correlations in patient electronic medical records (EMR) and other clinical information systems to assist clinicians in their diagnoses.
The overarching goal for TBI is to "develop informatics approaches for linking across traditionally disparate data and knowledge sources enabling both the generation and testing of new hypotheses".
[9] Current applications of TBI face challenges due to a lack of standards resulting in diverse data collection methodologies.
[6][9] Challenges also exist in the research of drugs and biomarkers, genomic medicine, protein design metagenomics, infectious disease discovery, data curation, literature mining, and workflow development.
[6] Continued belief in the opportunity and benefits of TBI justifies further funding for infrastructure, intellectual property protection and accessibility policies.