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Background Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizing maps are popular for their simplicity. However, because of noise and uncertainty of measurement,...
We propose an unsupervised approach for analyzing gene time-series datasets. Our method combines Affinity Propagation (AP) and the spirit of consensus clustering-- extracting multiple partitions from different time intervals. Without priori knowledge of total number of clusters and exemplars, this method holds the relationship between genes through different time intervals, and eliminates the influence...
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