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Review
. 2019 Sep 5;7(1):129.
doi: 10.1186/s40168-019-0742-2.

Challenges in the construction of knowledge bases for human microbiome-disease associations

Varsha Dave Badal  1 , Dustin Wright  1   2 , Yannis Katsis  3 , Ho-Cheol Kim  3 , Austin D Swafford  1 , Rob Knight  1   2   4   5 , Chun-Nan Hsu  6   7
Affiliations
Review

Challenges in the construction of knowledge bases for human microbiome-disease associations

Varsha Dave Badal et al. Microbiome. .

Abstract

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Keywords: Disease; Human; Knowledge base; Microbes; Microbiome; Microbiome dynamics; Model organisms; Natural language processing.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The rate of publications linking bacteria to human disease in PubMed. The chart displays the yearly count of PubMed abstracts matching human disease with microbes using the query (human AND disease) AND (microbiome OR microbiology OR microbes OR bacteria OR microbiota OR fungi OR virus). While the rise in publications began several decades ago, the last decade has featured a rapid increase in the number of publications spurred on by reductions in sequencing technologies and increased interest in the microbiome
Fig. 2
Fig. 2
a An example free-text snippet in a publication where an association between a bacterium and a disease is stated and can be systematically extracted by NLP and text mining techniques to construct a knowledge base. b An overview of the essential steps of text mining the literature for the construction of a knowledge base of human microbiome-disease associations
See this image and copyright information in PMC

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