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Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve...
In molecular biology, the selection of feature genes and tumor clustering are the hotspots and difficulties in bioinformatics research. The traditional PCA method based on the minimization of the squares of the loss function is sensitive to the outliers and noise. Therefore, it is necessary to design a new method to weaken the effects of errors and noise. In this paper, we propose a novel PCA method...
Nowadays, effective cancer treatment is still one of the most challenging missions in the scientific community. A primary limitation to most conventional anticancer drugs is their adverse uptake by normal tissues, fast metabolism and lack of tumour cell targeting property. One way to overcome these disadvantages is the application of nanoparticles as the carriers of widely known therapeutic substances...
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