Using GO-WAR for mining cross-ontology weighted association rules

tThe Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associatedto one or more gene products. The process of association is referred to as annotation. Therelevance and the specificity of both GO terms and annotations are evaluated by a measuredefined as information content (IC). The analysis of annotated data is thus an importantchallenge for bioinformatics. There exist different approaches of analysis. From those, theuse of association rules (AR) may provide useful knowledge, and it has been used in someapplications, e.g. improving the quality of annotations. Nevertheless classical associationrules algorithms do not take into account the source of annotation nor the importanceyielding to the generation of candidate rules with low IC. This paper presents GO-WAR(Gene Ontology-based Weighted Association Rules) a methodology for extracting weightedassociation rules. GO-WAR can extract association rules with a high level of IC without lossof support and confidence from a dataset of annotated data. A case study on using of GO-WAR on publicly available GO annotation datasets is used to demonstrate that our methodoutperforms current state of the art approaches.