The Application of Auto Regressive Time Varying Models to the Network Building of Time Series Microarray Data / 中国卫生统计

Objective To explore the application of auto regressive time varying models in network building of time se-ries microarray data.Methods We used actual data to carry out a preliminary discussion about the properties of auto regressive time varying models.Results Analysis results of actual data suggested that auto regressive time varying models can perform well whether the number of timepoint is large or small,and it can recognize the network’s dynamic variation rule.Conclusion Auto regressive time varying models is applicable to network building of time series microarray data.

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

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.