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• W4366606077 abstract "Abstract Motivation Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. Results We present a deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets. Availability and implementation The source code is publicly available at https://github.com/canc1993/graces." @default.
• W4366606077 created "2023-04-23" @default.
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• W4366606077 date "2023-04-01" @default.
• W4366606077 modified "2023-10-14" @default.
• W4366606077 title "Graph convolutional network-based feature selection for high-dimensional and low-sample size data" @default.
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• W4366606077 doi "https://doi.org/10.1093/bioinformatics/btad135" @default.
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