ArticleBMC bioinformatics2023
Covariance regression with random forests.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Integrating Genome-Wide Association Study (GWAS) and Marker-Assisted Selection for Enhanced Predictive Performance of Soybean Cold Tolerance.International journal of molecular sciences · 2025Article
- Identification of CDK2 as a key apoptotic gene for predicting cervical cancer prognosis using bioinformatics and machine learning.American journal of cancer research · 2025Article
- Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment.Statistical methods in medical research · 2024Article
- Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covariance matrix of a multivariate response given a set of covariates, using a random forest framework. Random forest trees are built with a splitting rule specially designed to maximize the difference between the sample covariance matrix estimates of the child nodes. We also propose a significance test for the partial effect of a subset of covariates. We evaluate the performance of the proposed method and significance test through a simulation study which shows that the proposed method provides accurate covariance matrix estimates and that the Type-1 error is well controlled. An application of the proposed method to thyroid disease data is also presented. CovRegRF is implemented in a freely available R package on CRAN.
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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.