ArticleIEEE/ACM transactions on computational biology and bioinformatics
Enriched Random Forest for High Dimensional Genomic Data.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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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
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
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- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.BMC genomics · 2026Article
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- Predictive factors of Black American emerging adults' psychological flourishing: a random forest analysis.Scientific reports · 2026Article
- Benchmarking Feature Selection Methods and Prediction Models for Flowering Time Prediction in Maize.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Transcriptomics and AI-driven approaches to the diagnosis and treatment of rheumatoid arthritis.Frontiers in immunology · 2026Review
- Article
- Integrating co-expression network analysis and machine learning to reveal the regulatory landscape ofPeerJ · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- A machine learning approach to identify at-risk populations for yellow fever vaccination.Journal of clinical and translational science · 2026Article
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- A computed tomography-based radiomics prediction model for BRAF mutation status in colorectal cancer.Abdominal radiology (New York) · 2025Article
- ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties.Scientific reports · 2025Article
- A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning.Scientific reports · 2025Article
- Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT.Healthcare informatics research · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Ensemble methods such as random forest works well on high-dimensional datasets. However, when the number of features is extremely large compared to the number of samples and the percentage of truly informative feature is very small, performance of traditional random forest decline significantly. To this end, we develop a novel approach that enhance the performance of traditional random forest by reducing the contribution of trees whose nodes are populated with less informative features. The proposed method selects eligible subsets at each node by weighted random sampling as opposed to simple random sampling in traditional random forest. We refer to this modified random forest algorithm as "Enriched Random Forest". Using several high-dimensional micro-array datasets, we evaluate the performance of our approach in both regression and classification settings. In addition, we also demonstrate the effectiveness of balanced leave-one-out cross-validation to reduce computational load and decrease sample size while computing feature weights. Overall, the results indicate that enriched random forest improves the prediction accuracy of traditional random forest, especially when relevant features are very few.
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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.