ArticleBioinformatics (Oxford, England)2020
Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.
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 11 papers.
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11 citing papers in PubMed.
- Influences of organic nitrogen application ratio on oil content in flue-cured tobacco based on field experiments and a random forest model.Frontiers in plant science · 2026Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics.Biomedicines · 2024Article
- Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.BioData mining · 2024Article
- Exploitation of surrogate variables in random forests for unbiased analysis of mutual impact and importance of features.Bioinformatics (Oxford, England) · 2023Article
- Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer.Frontiers in genetics · 2022Article
- Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth.Metabolites · 2021Article
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis.BioData mining · 2021Article
- A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer.Frontiers in genetics · 2021Article
- Application of random forest based approaches to surface-enhanced Raman scattering data.Scientific reports · 2020Article
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4 authors.
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Abstract
motivationHigh-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets.
resultsThe self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of interest. The hybrid approach synthetic features is not recommended because of its high false discovery rate. AVAILABILITY AND IMPLEMENTATION: An R package providing functions for data analysis and simulation is available at GitHub (https://github.com/szymczak-lab/PathwayGuidedRF). An accompanying R data package (https://github.com/szymczak-lab/DataPathwayGuidedRF) stores the processed and quality controlled experimental datasets downloaded from Gene Expression Omnibus (GEO). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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