Discovering relational-based association rules with multiple minimum supports on microarray datasets

Liu, Yucheng; Cheng, Chun-Pei; Tseng, Vincent S.

doi:10.1093/bioinformatics/btr526

Cited by 28 publications

(22 citation statements)

References 26 publications

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“…We also evaluated the location of ABs in a biological network derived from diverse data types such as protein-protein, gene regulatory, DNA-protein, and RNA-protein interactions, since some previous studies have indicated that mining associated gene-based patterns like association rules [4], co-expressed patterns [34] and sequential patterns [35,36] from single experiments are related in biological networks. Therefore, we chose to examine the distance between ABs in the IPA (Ingenuity® Systems, http://www.ingenuity.com) interaction network.…”

Section: Resultsmentioning

confidence: 99%

“…Another example is in causal SNP identification where Han et al showed that two associated SNPs in the non-coding region of CFH (complement factor H) were linked to age-related macular degeneration [3]. In our previous studies, we also demonstrated that co-expressed genes revealed from association rules are associated in yeast cells when they suffered from different stresses [4]. Therefore, many lines of evidence suggest that combination effects of certain genes influence biological outcomes rather than individual effects of a set of unassociated individual genes.…”

Section: Introductionmentioning

confidence: 97%

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MiningABs: mining associated biomarkers across multi-connected gene expression datasets

et al. 2014

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BackgroundHuman disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms.ResultsWe propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The similarity between paired probe sequences is quantified as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Our approach is evaluated with two sets of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma datasets. Based on an unbiased reciprocal test, we demonstrate that each gene in a group of ABs is required to maintain high cancer sample classification accuracy, and we observe that ABs are not limited to genes common to all platforms. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that our ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions.ConclusionsThe proposed meta-analysis method called MiningABs is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment. Java source code, tutorial, example and related materials are available at “http://sourceforge.net/projects/miningabs/”.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

MiningABs: mining associated biomarkers across multi-connected gene expression datasets

et al. 2014

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“…In recent years different strategies were proposed to apply the ARM algorithm to the analysis of microarray expression data [15,16]. In this paper we use a simplified version of the ARM algorithm, which is based on two main points: (i) we look for associations containing only two genes and (ii) one of the involved genes is constrained to be the gene selected by the user (Target gene).…”

Section: Methodsmentioning

confidence: 99%

CorrelaGenes: a new tool for the interpretation of the human transcriptome

et al. 2014

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BackgroundThe amount of gene expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to transform them in information easily accessible to biologists.ResultsBy exploiting expression data publicly available in the Gene Expression Omnibus (GEO) database, we developed a new bioinformatics tool aimed at the identification of genes whose expression appeared simultaneously altered in different experimental conditions, thus suggesting co-regulation or coordinated action in the same biological process. To accomplish this task, we used the 978 human GEO Curated DataSets and we manually performed the selection of 2,109 pair-wise comparisons based on their biological rationale. The lists of differentially expressed genes, obtained from the selected comparisons, were stored in a PostgreSQL database and used as data source for the CorrelaGenes tool. Our application uses a customized Association Rule Mining (ARM) algorithm to identify sets of genes showing expression profiles correlated with a gene of interest. The significance of the correlation is measured coupling the Lift, a well-known standard ARM index, and the χ2 p value. The manually curated selection of the comparisons and the developed algorithm constitute a new approach in the field of gene expression profiling studies. Simulation performed on 100 randomly selected target genes allowed us to evaluate the efficiency of the procedure and to obtain preliminary data demonstrating the consistency of the results.ConclusionsThe preliminary results of the simulation showed how CorrelaGenes could contribute to the characterization of molecular pathways and biological processes integrating data obtained from other applications and available in public repositories.

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“…However, in real applications, each item can have different nature, frequency and importance, and thus there is a need to consider the nature. To address this issue, association rule mining with multiple minimum supports [6][7][8]13,14,17,21,22] and the importance of items [2,16,18,[27][28][29][30]32] were proposed.…”

Section: Introductionmentioning

confidence: 99%

Discovering high utility itemsets with multiple minimum supports

Ryang

Yun

Ryu

2014

IDA

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Generally, association rule mining uses only a single minimum support threshold for the whole database. This model implicitly assumes that all items in the database have the same nature. In real applications, however, each item can have different nature such as medical datasets which contain information of both diseases and symptoms or status related to the diseases. Therefore, association rule mining needs to consider multiple minimum supports. Association rule mining with multiple minimum supports discovers all item rules by reflecting their characteristics. Although this model can identify meaningful association rules including rare item rules, not only the importance of items such as fatality rate of diseases but also attribute of items such as duration of symptoms are not considered since it treats each item with equal importance and represents the occurrences of items in transactions as binary values. In this paper, we propose a novel tree structure, called MHU-Tree (Multiple item supports with High Utility Tree), which is constructed with a single scan. Moreover, we propose an algorithm, named MHU-Growth (Multiple item supports with High Utility Growth), for mining high utility itemsets with multiple minimum supports. Experimental results show that MHU-Growth outperforms the previous algorithm on both real and synthetic datasets, and can discover useful rules from a medical dataset.

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Discovering relational-based association rules with multiple minimum supports on microarray datasets

Cited by 28 publications

References 26 publications

MiningABs: mining associated biomarkers across multi-connected gene expression datasets

MiningABs: mining associated biomarkers across multi-connected gene expression datasets

CorrelaGenes: a new tool for the interpretation of the human transcriptome

Discovering high utility itemsets with multiple minimum supports

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