Author Information

Lingling An
R. W. Doerge

Abstract

The cell cycle is a crucial series of events that are repeated over time, allowing the cell to grow, duplicate, and split. Cell-cycle systems play an important role in cancer and other biological processes. Using gene expression data gained from microarray technology it is possible to group or cluster genes that are involved in the cell-cycle for the purpose of exploring their functional co-regulation. Typically, the goal of clustering methods as applied to gene expression data is to place genes with similar expression patterns or profiles into the same group or cluster for the purpose of inferring the function of unknown genes that cluster with genes of known function. Since a gene may be involved in more than one biological process at any one time, co-regulated genes may not have visually similar expression patterns. Furthermore, the time duration for genes in a biological process may differ, and the number of co-regulated patterns or biological processes shared by two genes may be unknown. Based on this reasoning, biologically realistic gene clusters gained from gene co-regulation may not be accurately identified using traditional clustering methods. By taking advantage of techniques and theories from signal processing, it possible to cluster cell-cycle gene expression profiles using a dynamic perspective under the assumption that different spectral frequencies characterize different biological processes.

Keywords

clustering, dynamic, cell cycle, gene expression

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Apr 27th, 9:00 AM

DYNAMIC CLUSTERING OF CELL-CYCLE MICROARRAY DATA

The cell cycle is a crucial series of events that are repeated over time, allowing the cell to grow, duplicate, and split. Cell-cycle systems play an important role in cancer and other biological processes. Using gene expression data gained from microarray technology it is possible to group or cluster genes that are involved in the cell-cycle for the purpose of exploring their functional co-regulation. Typically, the goal of clustering methods as applied to gene expression data is to place genes with similar expression patterns or profiles into the same group or cluster for the purpose of inferring the function of unknown genes that cluster with genes of known function. Since a gene may be involved in more than one biological process at any one time, co-regulated genes may not have visually similar expression patterns. Furthermore, the time duration for genes in a biological process may differ, and the number of co-regulated patterns or biological processes shared by two genes may be unknown. Based on this reasoning, biologically realistic gene clusters gained from gene co-regulation may not be accurately identified using traditional clustering methods. By taking advantage of techniques and theories from signal processing, it possible to cluster cell-cycle gene expression profiles using a dynamic perspective under the assumption that different spectral frequencies characterize different biological processes.