Jul 12, 2016 · In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to ...

Abstract. In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to.

In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to select the ...

SSVD algorithm is applied to extract differentially expressed genes from two different genome datasets that are all from The Cancer Genome Atlas (TCGA), and ...

Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a ...

We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in ...

Sep 18, 2021 · In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address ...

It lays a foundation for further research on the relationship between cancer and genes in the molecular level and improves the efficiency of cancer diagnosis. .

Apr 15, 2021 · This paper proposes an integrative and sparse singular value decomposition (ISSVD) method for biclustering analysis in multi-sources datasets, ...

Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes ...