ArticleBMC bioinformatics2017
Improved high-dimensional prediction with Random Forests by the use of co-data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- Adaptive Use of Co-Data Through Empirical Bayes for Bayesian Additive Regression Trees.Statistics in medicine · 2025Article
- An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.Diabetologia · 2024Article
- Penalized regression with multiple sources of prior effects.Bioinformatics (Oxford, England) · 2023Article
- Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.Health science reports · 2023Article
- Semi-supervised empirical Bayes group-regularized factor regression.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood.Computational and structural biotechnology journal · 2022Article
- Adaptive group-regularized logistic elastic net regression.Biostatistics (Oxford, England) · 2021Article
- Screening and Identification of Potential Biomarkers for Hepatocellular Carcinoma: An Analysis of TCGA Database and Clinical Validation.Cancer management and research · 2020Article
- Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.Scandinavian journal of statistics, theory and applications · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPrediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting.
resultsCo-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study.
conclusionThe proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest.
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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.