Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers

Susanne Bornelöv1, Simon Marillet13 and Jan Komorowski12*

Author Affiliations

1 Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden

2 Institute of Computer Science, Polish Academy of Sciences, 01-248 Warsaw, Poland

3 Current address: INRIA Sophia-Antipolis-Méditerranée, Algorithms-Biology-Structure, Sophia-Antipolis, France

For all author emails, please log on.

BMC Bioinformatics 2014, 15:139  doi:10.1186/1471-2105-15-139

Published: 12 May 2014



The use of classification algorithms is becoming increasingly important for the field of computational biology. However, not only the quality of the classification, but also its biological interpretation is important. This interpretation may be eased if interacting elements can be identified and visualized, something that requires appropriate tools and methods.


We developed a new approach to detecting interactions in complex systems based on classification. Using rule-based classifiers, we previously proposed a rule network visualization strategy that may be applied as a heuristic for finding interactions. We now complement this work with Ciruvis, a web-based tool for the construction of rule networks from classifiers made of IF-THEN rules. Simulated and biological data served as an illustration of how the tool may be used to visualize and interpret classifiers. Furthermore, we used the rule networks to identify feature interactions, compared them to alternative methods, and computationally validated the findings.


Rule networks enable a fast method for model visualization and provide an exploratory heuristic to interaction detection. The tool is made freely available on the web and may thus be used to aid and improve rule-based classification.

Visualization; Rules; Interactions; Interaction detection; Classification; Rule-based classification