ArticleBioinformatics (Oxford, England)2020
forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- engGNN: a dual-graph neural network for omics-based disease classification and feature selection.Briefings in bioinformatics · 2026Article
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- Article
- engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection.ArXiv · 2026Article
- Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.BioData mining · 2025Article
- A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.Scientific reports · 2025Article
- Identify Diabetes-related Targets based on ForgeNet_GPC.Current computer-aided drug design · 2024Article
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Disease-related compound identification based on deeping learning method.Scientific reports · 2022Article
- Article
- Predicting residues involved in anti-DNA autoantibodies with limited neural networks.Medical & biological engineering & computing · 2022Article
- Active disease-related compound identification based on capsule network.Briefings in bioinformatics · 2022Article
- Alzheimer-Compound Identification Based on Data Fusion and forgeNet_SVM.Frontiers in aging neuroscience · 2022Article
- Disease-Ligand Identification Based on Flexible Neural Tree.Frontiers in microbiology · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
motivationA unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection.
resultsTo address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data. AVAILABILITY AND IMPLEMENTATION: The method is available at https://github.com/yunchuankong/forgeNet. CONTACT: tianwei.yu@emory.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.