Authors: Young-Rae Cho
Addresses: Computer and Telecommunication Division, Yonsei University - Mirae Campus, Wonju, Gangwon-do, South Korea
Abstract: The use of an ontology is a prevailing trend for management and analysis of biological big data. Consequently, we have encountered strong demands on developing algorithms for accurate analysis of ontology structures and annotated data. We can discover the association rules of cross-ontology terms, which provide the clues for predicting functions or phenotypes of a gene. However, because association rule mining algorithms are biased towards the rules of more general terms, it has been a challenge to discover the rules between more specific terms in concept. We propose a pairwise cross-ontology Weighted Rule Mining (WRM) approach which uses support and lift weighted by term specificity. For our experiments, Biological Process and Molecular Function sub-ontologies of Gene Ontology (GO), and Phenotypic Abnormality sub-ontology of Human Phenotype Ontology (HPO) were used. The results show IC-based WRM produced the rules of more specific terms in BP and PA than unweighted Association Rule Mining.
Keywords: ontology; gene ontology; HPO; human phenotype ontology; association rules; term specificity.
International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.3, pp.258 - 268
Received: 04 Sep 2020
Accepted: 14 Sep 2020
Published online: 21 Nov 2020 *