ArticleBioinformatics (Oxford, England)2018
A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed.
- Knowledge-driven interpretable neural networks provide mechanistic insight.Genome research · 2026Article
- engGNN: a dual-graph neural network for omics-based disease classification and feature selection.Briefings in bioinformatics · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
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- engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection.ArXiv · 2026Article
- Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.Briefings in functional genomics · 2026Article
- DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson's disease subtypes.BMC bioinformatics · 2025Article
- A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.Scientific reports · 2025Article
- A Selective Review of Network Analysis Methods for Gene Expression Data.Methods in molecular biology (Clifton, N.J.) · 2025Review
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- Article
- Identify Diabetes-related Targets based on ForgeNet_GPC.Current computer-aided drug design · 2024Article
- Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.Biomolecules · 2023Article
- Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes.International journal of molecular sciences · 2023Article
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Deep learning and multi-omics approach to predict drug responses in cancer.BMC bioinformatics · 2022Article
- Article
- EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Motivation: Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. Results: To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method's capability, we conducted both simulation experiments and real data analysis using a breast invasive carcinoma RNA-seq dataset and a kidney renal clear cell carcinoma RNA-seq dataset from The Cancer Genome Atlas. The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current graph-guided classification models and feature selection procedures. Availability and implementation: The method is available at https://github.com/yunchuankong/GEDFN. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.