Cancer gene expression data is of great importance in cancer subtype diagnosis and drug discovery. Many computational methods have been proposed to classify subtypes using those data. However, most of the previous computational methods suffer from poor interpretability, experimental noises, and low diagnostic quality.
Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing the sampling rate in the projection view angle in computed tomography (CT). Most of the image reconstruction algorithms developed for this purpose solve a nonsmooth convex optimization problem involving the image total variation (TV)…
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