Using GO-WAR for mining cross-ontology weighted association rules. Issue 2 (July 2015)

• Record Type: Journal Article
• Authors: Agapito, Giuseppe; Cannataro, Mario; Guzzi, Pietro Hiram; Milano, Marianna
• Is Part Of: Computer methods and programs in biomedicine. Volume 120:Issue 2(2015)
• Page Start: 113
• Page End: 122
• Publication Date: 2015-07
• Subjects: Association rule learning -- Gene Ontology -- Annotation quality -- Data mining; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.03.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: GO-WAR is a tool for learning cross-ontology weighted association rules. GO-WAR is currently the only tool available for this problem. GO-WAR is more flexible than previous approaches. A case study demonstrates the effectiveness of GO-WAR approach. Abstract: The Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associated to one or more gene products. The process of association is referred to as annotation. The relevance and the specificity of both GO terms and annotations are evaluated by a measure defined as information content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of association rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents GO-WAR (Gene Ontology-based Weighted Association Rules) a methodology for extracting weighted association rules. GO-WAR can extract association rules with a high level of IC without loss of 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 method outperforms current state of the art approaches.