Gene expression network discovery: a pattern based biclustering approach
Proceedings of the 2011 International Conference on Communication, Computing & Security
| Hi-index | 0.00 |
Mining biclusters that exhibit both consistent trends and trends with similar degrees of fluctuations is vital to bioinformatics research. However, existing biclustering methods are not very efficient and effective at mining such biclusters. Most biclustering models, including those used in subspace clustering, define similarity among different objects by distances over either all or only a subset of dimensions in gene expression data. However, distance functions are not always adequate in capturing co-relations among the objects. In fact, strong co-relations may still exist among a set of objects even if they are far apart from each other as measured by the distance function.Under the CPB (Coherent Pattern Biclustering) model, we proposed, two objects are similar if they exhibit coherent pattern on a subset of dimensions. For instances, in DNA microarray analysis, the expression levels of two genes may rise or fall synchronously in response to a set of environmental stimuli. Though the magnitude of their expression levels may not be close, but the pattern they exhibit can be very much similar. Our proposed model is interested in finding such coherent patterns of biclusters of genes and with a general understanding of biological processes that many genes participate in multiple different processes.