Mining inter-transaction association rules from time series microarray data / 第三军医大学学报

ABSTRACT

Objective To deduce the interactions between genes from time series microarray data.Methods We used inter-transaction association rules mining technique and GO (Gene Ontology) annotation to analyze the microarray data. Results Using 2-fold-change method, 119 differential expression genes were identified from total 10 080 genes or ESTs, whose expression levels varied significantly on 6 periods of fetus cerebellar development. As a result, about 1 300 inter-transaction association rules were extracted and 10 top rules were kept for their maximum J-measure values. A genes association network graph was deduced based on the 10 top rules. Conclusion Inter-transaction association rules are able to deduce the interactions between genes from time series microarray data and the gene expression status can be predicted based on the association rules.