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

Manda, Prashanti; McCarthy, Fiona M.; Bridges, Susan M.

doi:10.1016/j.jbi.2013.06.012

Cited by 33 publications

(22 citation statements)

References 17 publications

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“…The approach consists in combining data mining and statistical measurement techniques (such as redundancy analysis, sampling and multivariate statistical analysis) to discard the insignificant rules. Alcala-Fdez et al proposed a method based on rule covers to prune ARs [4]. The method defines subsets of rules describing the same transaction row.…”

Section: Objective and Subjective Methods For Ar's Post-processingmentioning

confidence: 99%

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Jeyanthi¹,

Pannirselvam²

2017

IOSR JCE

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Section: Objective and Subjective Methods For Ar's Post-processingmentioning

confidence: 99%

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Jeyanthi¹,

Pannirselvam²

2017

IOSR JCE

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“…Another approach where ARL from GO is used, is the work of Manda et al [15]. The methodology developed by Manda et al is based on a new algorithm called Multiontology data mining at All Levels (MOAL).…”

Section: Related Workmentioning

confidence: 99%

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

Agapito

Milano

Guzzi

et al. 2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

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“…They applied this method to a medical domain dataset, and through this means they demonstrated that the hybrid method provided a mechanism for reusing and automatically updating a knowledge base. The MOAL: Multi-Ontology data mining at All Levels algorithm [19] uses the structure and relationships of a Genetic Ontology to mine multi-ontology multi-level association rules. They introduce two interestingness measures: Multi-ontology support and Multi-ontology confidence customized to evaluate multi-ontology multilevel association rules.…”

Section: Related Workmentioning

confidence: 99%

MAROR: Multi-Level Abstraction of Association Rule Using Ontology and Rule Schema

Khiat¹,

Belbachir²,

Rahal³

2014

IJITCS

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Abstract-Many large organizations have multiple databases distributed over different branches. Number of such organizations is increasing over time. Thus, it is necessary to study data mining on multiple databases. Most multi-databases mining (MDBM) algorithms for association rules typically represent input patterns at a single level of abstraction. However, in many applications of association rules -e.g., Industrial discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to set of beliefs (and representational) commitments regarding the domain of interest. Using domain ontologies, we strengthen the integration of user knowledge in the mining and post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task at different levels. This paper formalizes the problem of association rules using ontologies in multi-database mining, describes an ontology-driven association rules algorithm to discoverer rules at multiple levels of abstraction and presents preliminary results in petroleum field to demonstrate the feasibility and applicability of this proposed approach.

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

Cited by 33 publications

References 17 publications

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

MAROR: Multi-Level Abstraction of Association Rule Using Ontology and Rule Schema

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