HIGHLIGHTS
- who: Bioinformatics ( et al. from the Hospital and Harvard Medical School, Boston, MA, USA, University of Illinois at Urbana-Champaign, Champaign, IL, USA have published the Article: Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data, in the Journal: (JOURNAL)
- what: The authors demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets. The authors propose a graph neural_network-based feature selection method - GRAph Convolutional nEtwork feature Selector (GRACES) - to extract features by exploiting the latent relations between samples for HDLSS data. All the . . .
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