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. 2012;8(12):e1002826.
doi: 10.1371/journal.pcbi.1002826. Epub 2012 Dec 27.

Chapter 1: Biomedical knowledge integration

Philip R O Payne  1
Affiliations

Chapter 1: Biomedical knowledge integration

Philip R O Payne. PLoS Comput Biol. 2012.

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.

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Conflict of interest statement

The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Key components of the KE process.
Figure 2
Figure 2. Ogden-Richards semiotic triad, illustrating the relationships between the three major semiotic-derived types of “meaning”.
Figure 3
Figure 3. Practical model for the design and execution of translational informatics projects, illustrating major phases and exemplary input or output resources and data sets.
Figure 4
Figure 4. Conceptual model for the generation of multi-network complexes of markers spanning a spectrum of granularity from bio-molecules to clinical phenotypes.
See this image and copyright information in PMC

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Grants and funding

The author received no specific funding for this article.