Information-theoretic Interestingness Measures for Cross-Ontology Data Mining

TL;DR

This paper introduces a novel data mining approach using ontology-guided generalization and an information-theoretic interestingness metric to discover high-quality relationships across biological ontologies, demonstrated on mouse genome annotation data.

Contribution

It presents a new ontology-guided data mining method and a novel interestingness metric based on information theory for cross-ontology relationship discovery.

Findings

Effective discovery of relationships between developmental stages and gene functions.

The proposed interestingness metric outperforms four existing metrics.

Application to mouse genome data demonstrates practical utility.

Abstract

Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.