  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.
