Mining Rare Associations between Biological Ontologies

Abstract

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

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“…For instance, Faria et al [9] proposed association rules to support GO curators by evaluating the annotation consistency in order to avoid possible inconsistent or redundant annotations. Diversely, Benites et al [10], proposed a data mining algorithm focused on mining rare associations from pairwise associations among multiple categories, useful to describe relations that are not obvious. We also proposed in the past GO-WAR [11], a datamining strategy based on weighted-association rule mining to support GO curators.…”

Using GO-WAR for mining cross-ontology weighted association rules

“…In this section, we summarize main differences between our methodology and the current state of the art focusing on Faria et al approach, [8], Manda et al [23], and Benites et al [12]. We recall that GO-WAR is slightly different from the method described in [8] since the authors are interested in capturing implicit relationships between aspects of a single function (e.g.…”

“…Benites et al [25], propose a data mining algorithm focused on mining rare associations from pairwise associations among multiple categories, useful to describe relations that are not obvious. Authors in their methodology introduced a new measure named: Interestingness by Differences instead to use the confidence.…”

“…From those, the use of association rules (AR) [4][5][6] is less popular with respect to other techniques, such as statistical methods or semantic similarities [7]. Existing approaches span from the use of AR to improve the annotation consistency, as presented in [8], to the use of AR to analyze microarray data [9][10][11][12][13][14], (see [15] for a detailed review). As we pointed out in a previous work [5], the use of AR presents two main issues due to the Number and the Nature of Annotations [16].…”