An ontology of chemical entities helps identify dependence relations among Gene Ontology terms. Semantic mining in biomedicine

independent hierarchies: molecular functions, biological processes, and cellular components. In this paper, we present an approach based on the Chemical Entities of Biological Interest (ChEBI) ontology to identifying dependence relations in GO, especially relations across hierarchies. Our method is based on the identification of the names of ChEBI entities in GO terms. We distinguish between firstorder dependence relations between GO terms that share a common chemical name, and second-order dependence relations between GO entities whose names include two chemicals that are hierarchically related. Of the 10,516 entities in ChEBI, 26 % were identified in the names of 9,431 GO terms (55 % of all GO terms). A total of 771,302 pairs of related GO terms (first-order associations) were computed. Of these, 44 % correspond to dependence relations across hierarchies. These results were compared to the 8,714 pairs of GO terms identified as dependent by lexical and statistical methods in a previous study (once restricted to GO terms whose names include a ChEBI entity). Of these, 3,932 (45%) were identified as first-order relations, and 937 (11%) as secondorder relations. We show that the two kinds of approaches are complementary. The ChEBI-based is independent of the annotations, allowing even rare dependencies to be identified. Moreover, it takes advantage of the subsumption relations between chemicals in ChEBI, and therefore helps identify second-order dependence relations. This approach can be generalized to other ontologies of chemicals as well as other kinds of ontologies