Bagley College of Engineering Publications and Scholarship


The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.


Public Library of Science

Publication Date



College of Agriculture and Life Sciences| College of Veterinary Medicine| James Worth Bagley College of Engineering


Department of Basic Sciences| Department of Computer Sciences and Engineering| Department of Plant and Soil Sciences


Algorithms, Animals, Chickens, Computational Biology, Computational Biology: methods, Data Mining, Data Mining: methods, Databases, Genetic, Mice, Molecular Sequence Annotation, Molecular Sequence Annotation: methods


Computational Biology