title: A new approach for clustering gene expression time series data.
abstract: Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for finding clusters in gene expression time series data using the new dissimilarity measure. A comparison with other similarity measures used for gene expression data is presented; the new dissimilarity measure is found effective. The clustering method is used in experiments that use real-life datasets and has been found to perform satisfactorily.
authors.affiliation: Department of Computer Science and Engineering, Tezpur University, Napaam 784028, Assam, India. rosy8@tezu.ernet.in
authors.forename: Rosy
authors.initials: R
authors.lastname: Das
authors.forename: Jugal
authors.initials: J
authors.lastname: Kalita
authors.forename: Dhruba K
authors.initials: DK
authors.lastname: Bhattacharyya
mesh_terms: D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods