Translational Bioinformatics is the subfield of Bioinformatics that involves the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health. Translational Bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients.
- In the study of health informatics
- Focused on the convergence of molecular
- Used in biostatistics, statistical genetics and clinical informatics
- Focuses 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
- It includes the use of biomedical research to improve human health through the use of computer-based information systems.
- Uses data mining and analyzing biomedical informatics in order to produce clinical knowledge for application
- Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes
Translational Bioinformatics is a relatively young and recent field within translational research.
At present, Translational Bioinformatics research spans multiple disciplines; however, the application of TBI in clinical settings remains limited. Currently, it is partially deployed in drug development, regulatory review, and clinical medicine. The opportunity for application of Translational Bioinformatics is much broader as increasingly medical journals are mentioning the term “informatics” and discussing bioinformatics related topics.
- General topics that covered in Translational Bioinformatics are:
- personal genomics and genomic infrastructure
- drug and gene research for adverse events, interactions and repurposing of drugs
- biomarkers and phenotype representation
- sequencing, science and systems medicine
- computational and analytical methodologies for Translational Bioinformatics
To extract relevant data from large data sets, it uses various methods such as data consolidation, data federation, and data warehousing. In the data consolidation approach, data is extracted from various sources and centralized in a single database. This approach enables standardization of heterogeneous data and helps address issues in interoperability and compatibility among data sets. However, proponents of this method often encounter difficulties in updating their databases as it is based on a single data model. In contrast, the data federation approach links databases together and extracts data on a regular basis, then combines the data for queries. The benefit of this approach is that it enables the user to access real-time data on a single portal.
Translational bioinformatics represents the advancement and sophistication of traditional in-silico approaches and focuses on applying the existing research to bridge biological data and clinical informatics. The applicability of Translational Bioinformatics has been expanded to both the biological and healthcare industries. Translational Bioinformatics has made certain databases accessible to researchers as information sources. These databases are valuable resources to many stakeholders including but not limited to clinicians, biologists, clinical researchers, bioinformaticians and health service researchers. For example, these databases are very useful for biologists to understand disease management and drug development methodologies useful in producing new hypotheses. Translational Bioinformatics databases in general are a collection of highly curated scientific evidence knowledgebase gathered from various experiments in combination with existing knowledge on descriptive genomics, proteomics, enzymes and gene variants.