Efficient mining of multilevel gene association rules from microarray and gene ontology

Abstract

Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are upregulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules.

Mining Rare Associations between Biological Ontologies

“…This is an essential drawback for biological applications [8], [9]. One of the reasons is that support filtering eliminates low support rules, classifying them as uninteresting.…”

“…Generally, traditional AR mining algorithms such as, e.g., Apriori [7] with the standard support-confidence approach, generate a huge amount of associations, which are largely redundant. This is an essential drawback for biological applications [8] , [9] . One of the reasons is that support filtering eliminates low support rules, classifying them as uninteresting.…”

“…And yet, rising interest in AR mining has been noted over the last decade, for example in the analysis of micro-array data. Several studies have been recently conducted to find groups of co-expressed genes by means of association analysis as an alternative to widely used clustering methods [3] , [8] , [16] – [20] . In this context, ARs can describe relations between expression levels of genes and certain cellular conditions – which genes are overexpressed or underexpressed in diseased cells as compared to healthy ones, for example.…”

Practical Approaches for Mining Frequent Patterns in Molecular Datasets

“…The advantage of this approach is that the rules immediately explain why a particular label was given, which is an advantage over machine learning methods such as neural networks that act as a black box. The strengths of frequent itemset mining have been consequently demonstrated in a broad range of bioinformatics applications, ranging from gene expression data, [6][7][8] annotation mining, 9,10 and combinations thereof 11,12 to interaction networks. 13 A comprehensive overview of the broad range of implementations and bioinformatics applications of frequent itemset mining techniques was recently published.…”

Mining disease integrated ontology

“…Previous research in concept level mining research can be found at [8,9], concerning mining association rules from traditional relational databases with the use of ontologies to refine and improve the resulting rules. However, Tseng et al [10] proposed a new algorithm called, MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). It can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO.…”