Abstract
The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.
Highlights
The modern biomedical research domain has experienced a fundamental shift towards integrative and translational methodologies and frameworks over the past several years
The development and execution of multi-disciplinary, translational science programs is significantly limited by the propagation of ‘‘silos’’ of both data and knowledge, and a paucity of reproducible and rigorously validated methods that may be used to support the satisfaction of motivating and integrative translational bioinformatics use cases, such as those focusing on the identification of expression motifs spanning bio-molecules and clinical phenotypes
As was stated at the outset of this chapter, our goals were to review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems
Summary
The modern biomedical research domain has experienced a fundamental shift towards integrative and translational methodologies and frameworks over the past several years. A common thread throughout the translational sciences are needs related to the collection, management, integration, analysis and dissemination of large-scale, heterogeneous biomedical data sets. Well-established and broadly adopted theoretical and practical frameworks intended to address such needs are still largely developmental [1,2,3]. In order to provide sufficient context and scope to our ensuing discussion, we will define translational science and research per the conventions provided by the National Institutes of Health (NIH) as follows:. ‘‘Translational research includes two areas of translation. The second area of translation concerns research aimed at enhancing the adoption of best practices in the community. Cost-effectiveness of prevention and treatment strategies is an important part of translational science.’’ [4]
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