ArticleBioinformatics (Oxford, England)2010
On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 137 papers.
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Who cites it
137 citing papers in PubMed.
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- Importance of medical home domains on emergency visits using a cross-sectional national survey of US children.BMJ open · 2024Article
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- Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder.Translational psychiatry · 2024Observational
- Rearing of Glossina morsitans morsitans tsetse flies for the sterile insect technique: evaluating the impact of irradiation and transportation during early and late-stage pupal development on the quality of emerging adults.Parasite (Paris, France) · 2024Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
motivationGenome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden.
resultsHere, we present the new freely available software package Random Jungle (RJ), which facilitates the rapid analysis of GWA data. The program yields valid results and computes up to 159 times faster than the fastest alternative implementation, while still maintaining all options of other programs. Specifically, it offers the different permutation importance measures available. It includes new options such as the backward elimination method. We illustrate the application of RJ to a GWA of Crohn's disease. The most important single nucleotide polymorphisms (SNPs) validate recent findings in the literature and reveal potential interactions. AVAILABILITY: The RJ software package is freely available at http://www.randomjungle.org
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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.