Semi supervised fuzzy clustering networks for constrained analysis of time-series gene expression data

Clustering analysis of time series data from DNA microarray hybridization studies is essential for identifying biological relevant groups of genes. Microarrrays provide large datasets that are currently primarily analyzed using crisp clustering techniques. Crisp clustering methods such as K-means or self organizing maps assign each gene to one cluster, thus omitting information concerning the multiple roles of genes. One of the major advantages of fuzzy clustering is that genes can belong to more than one group, revealing this way more profound information concerning the function and regulation of each gene. Additionally, recent studies have proven that integrating a small amount of information in purely unsupervised algorithms leads to much better performance. In this paper we propose a new semi-supervised fuzzy clustering algorithm which we apply in time series gene expression data. The clustering that was performed on simulated as well as experimental microarray data proved that the proposed method outperformed other clustering techniques.