Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text

A. Bin Raies, H. Mansour, R. Incitti, V.B.Bajic
PLoS One, 8(10):e77848, (2013)

Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text

Keywords

 Position weight matrices

Abstract

​Background

 

In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually.

Methodology

 

We developed a novel text mining methodology based on a new concept of position weight matrices (PWMs) for text representation and feature generation. We applied PWMs in conjunction with the document-term matrix to extract with high accuracy associations between methylated genes and diseases from free text. The performance results are based on large manually-classified data. Additionally, we developed a web-tool, DEMGD, which automates extraction of these associations from free text. DEMGD presents the extracted associations in summary tables and full reports in addition to evidence tagging of text with respect to genes, diseases and methylation words. The methodology we developed in this study can be applied to similar association extraction problems from free text.

Conclusion

 

The new methodology developed in this study allows for efficient identification of associations between concepts. Our method applied to methylated genes in different diseases is implemented as a Web-tool, DEMGD.

Code

DOI: 10.1371/journal.pone.0077848

Sources

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