Abstract This study presents a novel approach to detect biological associations for gene pairs with cell cycle-specific expression profiles. Previous studies have shown that periodic transcription is commonly regulated by transcription factors that are also periodically transcribed, and there is a growing number of examples where cell cycle regulated genes are conserved in yeast and mammalian cells. Some genes have periodicity for their oscillatory activity throughout cell division. These cell cycle-specific oscillatory activities could be explained by a biological phenomenon in terms of efficiency and logical order. In the yeast data used in this study, about 13% of genes behave in this manner based on a previous yeast study. Microarrays have been applied to determine genome-wide expression patterns during the cell cycle of a number of different cells. Moreover, several previous studies have shown that many pairs of genes, which have linearly correlated expression profiles, have similar cellular roles or physical interactions. Based on this point of view, the traditional clustering methods have focused on similar expression profiles based on the premise that genes with similar expression profiles have similar biological functions or relevant biological interactions. However, there are a number of previous studies indicating that the expression of some genes may be delayed compared to others due to a time lag in their transcriptional control. Therefore, we propose a novel clustering method, named as phase-synchronization clustering, based on the theory of phase synchronization for detecting biological associations using cell cycle-specific expression profiles. We evaluate phase-synchronization clustering here using Saccharomyces cerevisiae microarray data. Phase-synchronization clustering is able to detect biologically associated gene pairs that have linearly correlated (simultaneous and inverted) as well as time-delayed expression profiles. The performance of phase-synchronization clustering is compared with other conventional clustering methods. The likelihood of finding relevant biological associations by phase-synchronization clustering is significantly higher than other clustering methods. Therefore, phase-synchronization clustering is more efficient for detecting known biological interactions for gene pairs than other conventional clustering methods for analyzing cell cycle-specific expression data. The evaluation analysis of the results by phase-synchronization clustering also suggests that the cellular activities during the cell division process could be understood as a phenomenon of collective synchronization.