Efficient estimation of covariances and dependencies in high-dimensional gene expression data

Abstract

The learning of dependencies in microarray data is challenging. Here, we will give a review of estimation methods based on Stein-type shrinkage. At their core lies a regularized estimation of the covariance matrix of the data. Subsequently, genetic networks from both, static and time-series data, can be inferred. The algorithms described exhibit a high… (More)

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