ArticleBiomolecules2023
Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Predicting Capsid Protein Binding Sites in Single-Stranded RNA Viruses Using Machine Learning from Local Geometric Features.bioRxiv : the preprint server for biology · 2026Article
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- Machine learning-based analysis of prognostic factors in patients with tuberculous meningitis.BMC infectious diseases · 2026Article
- The human skin microbiome: factors affecting individuality and application in forensic investigations.International journal of legal medicine · 2026Review
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
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- Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.Scientific reports · 2025Article
- Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2025Article
- The Key Role and Mechanism of Oxidative Stress in Hypertrophic Cardiomyopathy: A Systematic Exploration Based on Multi-Omics Analysis and Experimental Validation.Antioxidants (Basel, Switzerland) · 2025Article
- Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.BioData mining · 2025Article
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- Mechanistic investigation of Shuanghuanglian against infectious bronchitis in chickens: a network pharmacology and molecular dynamics study.Frontiers in veterinary science · 2025Article
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- Effective Analysis of Alzheimer's Disease and Mechanisms of Methyl-4- Hydroxybenzoate using Network Toxicology, Molecular Docking, and Machine Learning Strategies.Current Alzheimer research · 2025Article
- Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.Foods (Basel, Switzerland) · 2024Article
- Machine learning-based prognostic prediction for hospitalized HIV/AIDS patients with cryptococcus infection in Guangxi, China.BMC infectious diseases · 2024Article
- Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.Oral radiology · 2024Article
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- Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms.Lipids in health and disease · 2024Article
- Evaluation of drug-drug interaction between Suraxavir Marboxil (GP681) and itraconazole, and assessment of the impact of gene polymorphism.Frontiers in pharmacology · 2024Article
Corrections and comments
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets-non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures.
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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.