Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
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Authors: | Tianqing Liu Nan Lin Ningzhong Shi Baoxue Zhang |
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Institution: | (1) Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR, China;(2) Department of Mathematics, Washington University in St Louis, St Louis, USA |
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Abstract: | Background Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes
based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering
algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive
detection of genes that display a consistent pattern over time. Peddada et al. 1] proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference.
This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large
number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important
measure when clustering noisy microarray data. |
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Keywords: | |
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