ArticleBMC bioinformatics2009
Identification of temporal association rules from time-series microarray data sets.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 29 citations in OpenAlex.
- Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.Biomedicines · 2024Article
- Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.Sensors (Basel, Switzerland) · 2024Article
- eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.PLoS computational biology · 2020Article
- Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.BMC bioinformatics · 2015Article
- Mining differential top-k co-expression patterns from time course comparative gene expression datasets.BMC bioinformatics · 2013Article
- Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.PloS one · 2012Article
- Discovering time-lagged rules from microarray data using gene profile classifiers.BMC bioinformatics · 2011Article
- Gene set enrichment; a problem of pathways.Briefings in functional genomics · 2010Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A upward arrow, gene B downward arrow] --> (7 min) [gene C upward arrow], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.
resultsIn the parameter fitting phase of TARM, the fitted parameter set [threshold = +/- 0.8, support >or= 3 transactions, confidence >or= 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.
conclusionIn this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.
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