Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

Prashanti Manda, Fiona McCarthy, Susan M. Bridges

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support ( MOSupport) and Multi-ontology Confidence ( MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.

Original languageEnglish (US)
Pages (from-to)849-856
Number of pages8
JournalJournal of Biomedical Informatics
Volume46
Issue number5
DOIs
StatePublished - Oct 1 2013

Keywords

  • Association rule mining
  • Data mining
  • Gene ontology
  • Gene ontology relationships
  • Interestingness measures
  • Interpro relationships

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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