This article presents a new method for analyzing microarray time courses by identifying genes that undergo abrupt transitions in expression level, and the time at which the transitions occur. The algorithm matches the sequence of expression levels for each gene against temporal patterns having one or two transitions between two expression levels. The algorithm reports a P-value for the matching...

This paper describes how to discover dynamic relationships among genes from time series microarray data with association rule mining approach. To hold dynamic information in the rules, the association rules were extracted using the constraints that expression level of genes appear in the antecedent and change direction of expression level of genes in the consequent. Besides, we have applied fuz...

Microarrays are powerful tools for surveying the expression levels of many thousands of genes simultaneously. They belong to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. There are myriad sources of uncertainty in microarray experiments, and rigorous experimental design is essential for fully realizing the potential ...

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course microarray experiments in which gene expression is monitored over time, we are interested in clustering genes that show similar temporal profiles and identifying genes that show a pre-specified profile. Unfortunately, many traditional clustering methods used for analyzing the m...

A truly functional Bayesianmethod for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample is a modification of the first, i.e. the two samples are non-interchangeable. This distinction leads to...

While the rise of microarrays has heralded a new era in molecular biology with its ability to measure the expression level of thousands of genes at once, the usefulness of microarrays is exigent upon the ability to obtain accurate gene expression data for the individual genes (Bowtell, 1999; Brown & Botstein, 1999; Cheung, Morley, Aguilar, Massimi, Kucherlapati, & Childs, 1999). However, there ...