Learning Weighted Association Rules in Human Phenotype Ontology

TL;DR

This paper introduces HPO-Miner, a novel method for extracting weighted association rules from Human Phenotype Ontology data, improving the relevance of discovered rules by considering annotation importance, demonstrated through a case study.

Contribution

HPO-Miner is the first approach to incorporate annotation importance into association rule mining for HPO data, enhancing biological relevance.

Findings

HPO-Miner effectively extracts biologically relevant rules.

Weighted rules outperform classical methods in relevance.

Case study confirms improved annotation quality.

Abstract

The Human Phenotype Ontology (HPO) is a structured repository of concepts (HPO Terms) that are associated to one or more diseases. The process of association is referred to as annotation. The relevance and the specificity of both HPO terms and annotations are evaluated by a measure defined as Information Content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of Association Rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents HPO-Miner (Human Phenotype Ontology-based Weighted Association Rules) a methodology for extracting Weighted Association Rules. HPO-Miner can extract relevant rules from a biological point of view. A case study on using of HPO-Miner on publicly available HPO annotation datasets is used to demonstrate the effectiveness of our methodology.