ArticleGenome biology2002
Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.
Article in Genome biology, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States.Journal of exposure science & environmental epidemiology · 2017Article
- Expression Data Analysis for the Identification of Potential Biomarker of Pregnancy Associated Breast Cancer.Pathology oncology research : POR · 2017Article
- Dynamic association rules for gene expression data analysis.BMC genomics · 2015Article
- A primer to frequent itemset mining for bioinformatics.Briefings in bioinformatics · 2015Review
- An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets.BioMed research international · 2015Article
- Mining TCGA data using Boolean implications.PloS one · 2014Article
- Mining rare associations between biological ontologies.PloS one · 2014Article
- MIDClass: microarray data classification by association rules and gene expression intervals.PloS one · 2013Article
- Fast rule-based bioactivity prediction using associative classification mining.Journal of cheminformatics · 2012Article
- Discovering associations in biomedical datasets by link-based associative classifier (LAC).PloS one · 2012Article
- Discovery of error-tolerant biclusters from noisy gene expression data.BMC bioinformatics · 2011Article
- Insights gained from the reverse engineering of gene networks in keloid fibroblasts.Theoretical biology & medical modelling · 2011Article
- Association rule based similarity measures for the clustering of gene expression data.The open medical informatics journal · 2010Article
- Translational bioinformatics and healthcare informatics: computational and ethical challenges.Perspectives in health information managemen · 2009Article
- Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum.Bioinformatics (Oxford, England) · 2009Article
- Identification of temporal association rules from time-series microarray data sets.BMC bioinformatics · 2009Article
- Stem cell antigen 2: a new gene involved in the self-renewal of erythroid progenitors.Cell proliferation · 2008Article
- SQUAT: A web tool to mine human, murine and avian SAGE data.BMC bioinformatics · 2008Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited to analysis of large gene-expression matrices. To validate the ARD technique we have applied it to freely available human serial analysis of gene expression (SAGE) data.
resultsThe approach described here enables us to designate sets of strong association rules. We normalized the SAGE data before applying our association rule miner. Depending on the discretization algorithm used, different properties of the data were highlighted. Both common and specific interpretations could be made from the extracted rules. In each and every case the extracted collections of rules indicated that a very strong co-regulation of mRNA encoding ribosomal proteins occurs in the dataset. Several rules associating proteins involved in signal transduction were obtained and analyzed, some pointing to yet-unexplored directions. Furthermore, by examining a subset of these rules, we were able both to reassign a wrongly labeled tag, and to propose a function for an expressed sequence tag encoding a protein of unknown function.
conclusionsWe show that ARD is a promising technique that turns out to be complementary to existing gene-expression clustering techniques.
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