OmicsARules: a R package for integration of multi-omics datasets via association rules mining

Background The improvements of high throughput technologies have produced large amounts of multi-omics experiments datasets. Initial analysis of these data has revealed many concurrent gene alterations within single dataset or/and among multiple omics datasets. Although powerful bioinformatics pipelines have been developed to store, manipulate and analyze these data, few explicitly find and assess the recurrent co-occurring aberrations across multiple regulation levels. Results Here, we introduced a novel R-package (called OmicsARules) to identify the concerted changes among genes under association rules mining framework. OmicsARules embedded a new rule-interestingness measure, Lamda3, to evaluate the associated pattern and prioritize the most biologically meaningful gene associations. As demonstrated with DNA methlylation and RNA-seq datasets from breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA) and lung adenocarcinoma (LUAD), Lamda3 achieved better biological significance over other rule-ranking measures. Furthermore, OmicsARules can illustrate the mechanistic connections between methlylation and transcription, based on combined omics dataset. OmicsARules is available as a free and open-source R package. Conclusions OmicsARules searches for concurrent patterns among frequently altered genes, thus provides a new dimension for exploring single or multiple omics data across sequencing platforms.

Visualization support for the analysis of properties of interestingness measures

The paper considers a particular group of rule interestingness measures, called Bayesian confirmation measures, which have become the subject of numerous, but often exclusively theoretical studies. To assist and enhance their analysis in real-life situations, where time constraints may impede conducting such time consuming procedures, a visual technique has been introduced and described in this paper. It starts with an exhaustive and non-redundant set of contingency tables, which consists of all possible tables having the same number of observations. These data, originally 4-dimensional, may, owing to an inherent constraint, be effectively represented as a 3-dimensional tetrahedron, while an additional, scalar function of the data (e.g. a confirmation measure) may be rendered using colour. Dedicated analyses of particular colour patterns on this tetrahedron allow to promptly perceive particular properties of the visualized measures. To illustrate the introduced technique, a set of 12 popular confirmation measures has been selected and visualized. Additionally, a set of 9 popular properties has been chosen and the visual interpretations of the measures in terms of the properties have been presented.

An Interestingness Measure and Computation Method of Association Rules Based on Frequent Itemsets Relatedness

To address inadequacy of association rules interestingness measure method currently, we present a novel method to measure interestingness with relatedness among items in frequent itemsets. It firstly computed relatedness between frequent k-itemsets and each subset of frequent 2-itemsets, which is a linear combination of Complementarity Intensity (CI), Substitutability Intensity (SI) and Mutual Interaction (MI). The mean of relatedness of all frequent 2-itemsets subsets was regarded as relatedness of frequent k-itemsets. Finally weighted computation method of association rule interestingness was given according to principle of objective interestingness of association rule is inversely proportional to relatedness of frequent itemsets. The method can not only sort rules, but also analyze actual relationship among all items in frequent 2-itemsets, which is conductive to selection of users on rules.

Grid-Based Platform for Mining Association Rules

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.

STUDYING INTEREST MEASURES FOR ASSOCIATION RULES THROUGH A LOGICAL MODEL

Many papers have addressed the task of proposing a set of convenient axioms that a good rule interestingness measure should fulfil. We provide a new study of the principles proposed until now by means of the logic model proposed by Hájek et al.14 In this model association rules can be viewed as general relations of two itemsets quantified by means of a convenient quantifier.28 Moreover, we propose and justify the addition of two new principles to the three proposed by Piatetsky-Shapiro.27 We also use the logic approach for studying the relation between the different classes of quantifiers and these axioms. We define new classes of quantifiers according to the notions of strong and very strong rules, and we present a quantifier based on the certainty factor measure,317 studying its most salient features.