ArticleBioinformatics (Oxford, England)2023
Graph convolutional network-based feature selection for high-dimensional and low-sample size data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Microbiome research in liver transplantation.Hepatobiliary surgery and nutrition · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- A systems microbiology framework for reproducible multi-dataset omics integration with application to long COVID.Frontiers in systems biology · 2026Article
- Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.Scientific reports · 2025Article
- Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer.Scientific reports · 2025Article
- BIM-Ken: Identifying Disease-Related miRNA Biomarkers Based on Knowledge-Enhanced Bio-Network.Genes · 2025Article
- Spatially resolved subcellular protein-protein interactomics in drug-perturbed lung-cancer cultures and tissues.Nature biomedical engineering · 2025Article
- An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets.BMC bioinformatics · 2024Article
- Biobjective gradient descent for feature selection on high dimension, low sample size data.PloS one · 2024Article
- Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationFeature 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.
resultsWe 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.
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