Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. iXtractR ("I eXtract Relations") is an implementation of this framework. It is a web service designed to detect the various types of relations/events: Gene_expression, Transcription, Localization, Phosphorylation, Protein_catabalism, and Binding. You can download the software package here.

To illustrate the usefulness of iXtractR, Figure 1 and 2 give samples of the annotations in sentences from the biomedical literature.

Figure 1

Figure 2

Citing iXtractR

The main technical ideas behind how iXtractR works appear in these papers. Feel free to cite one or more of the following papers depending on what you are using.


Research reported in this website was supported by the National Library of Medicine of the National Institutes of Health under award number G08LM010720. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

This material is also based upon work supported by the National Science Foundation under Grant No. DBI-1062520. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Release history

Version 0.1a 2017-05-22 Initial release
Version 0.1 2014-04-21 Initial release
Version 0.1.1 2014-12-09 Add citation