Translational informatics: enabling high-throughput research paradigms

Abstract

A common thread throughout the clinical and translational research domains is the need to collect, manage, integrate, analyze, and disseminate large-scale, heterogeneous biomedical data sets. However, well-established and broadly adopted theoretical and practical frameworks and models intended to address such needs are conspicuously absent in the published literature or other reputable knowledge sources. Instead, the development and execution of multidisciplinary, clinical, or translational studies are significantly limited by the propagation of "silos" of both data and expertise. Motivated by this fundamental challenge, we report upon the current state and evolution of biomedical informatics as it pertains to the conduct of high-throughput clinical and translational research and will present both a conceptual and practical framework for the design and execution of informatics-enabled studies. The objective of presenting such findings and constructs is to provide the clinical and translational research community with a common frame of reference for discussing and expanding upon such models and methodologies.

Fig. 1.

Overview of our 4-phase approach, highlighting sources of information (input), findings, or knowledge products (output) and the relationships between such components.

Fig. 2.

Translational research cycle, illustrating the T1 and T2 “translational blocks,” as described by Sung and colleagues (72).

Fig. 3.

Conceptual framework incorporating: 1) major information types and needs involved in the creation of translational biomedical knowledge and 2) a high-level overview of the 3-phase process by which knowledge and information from each such source is utilized to enable the generation of such knowledge.

Fig. 4.

Practical model for the design and execution of translational informatics projects, illustrating major phases and exemplary input or output resources and data sets.