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Review
. 2014:2014:253128.
doi: 10.1155/2014/253128. Epub 2014 Apr 16.

A knowledge-driven approach to extract disease-related biomarkers from the literature

À Bravo  1 , M Cases  1 , N Queralt-Rosinach  1 , F Sanz  1 , L I Furlong  1
Affiliations
Review

A knowledge-driven approach to extract disease-related biomarkers from the literature

À Bravo et al. Biomed Res Int. 2014.

Abstract

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

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Figures

Figure 1
Figure 1
Text mining workflow.
Figure 2
Figure 2
An example of the variability in terminology for genes depending on the primary sources.
Figure 3
Figure 3
Number of publications (bars) and number of journals (line) by year.
Figure 4
Figure 4
The top 10 journals sorted by unique disease-biomarker cooccurrences identified.
Figure 5
Figure 5
Associations analysis. (a) Boxplot showing the score versus number of publications supporting each disease-biomarker association. (b) Distribution of associations based on the number of publications that support each association. The fraction of the associations that were reported in the last three years is highlighted as dark grey bars.
Figure 6
Figure 6
Distribution of the number of associated biomarkers (for diseases, (a)) and diseases (for biomarkers, (b)). Gene symbols from HGNC are used for the biomarkers.
See this image and copyright information in PMC

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