Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJBRA.2009.026422 | IJBRA |
Keywords | Field | DocType |
series data,identifying group,clustering gene expression time,similar expression pattern,clustering method,suitable dissimilarity measure,graph-based clustering method,gene expression data,similarity measure,new dissimilarity measure,new approach,gene expression time series,time series data,bioinformatics,gene expression,microarrays | Time series,Data mining,Fuzzy clustering,Similarity measure,Consensus clustering,Artificial intelligence,Cluster analysis,Correlation clustering,Pattern recognition,Gene expression,Bioinformatics,Mathematics,DNA microarray | Journal |
Volume | Issue | ISSN |
5 | 3 | 1744-5485 |
Citations | PageRank | References |
9 | 0.57 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rosy Das | 1 | 13 | 1.67 |
| Jugal Kalita | 2 | 249 | 21.60 |
| Dhruba K. Bhattacharyya | 3 | 226 | 27.72 |