On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
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MOTIVATION: Genome-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. RESULTS: Here, 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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ATHENA uses grammatical evolution neural networks with Biofilter and Random Jungle to investigate categorical and quantitative variables and develop prediction models.
“It uses grammatical evolution neural networks along with Biofilter (Bush et al., 2009) and Random Jungle (Schwarz et al., 2010) to investigate different categorical and quantitative variables and develop prediction models.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Schwarz et al., 2010
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- Authors
- Schwarz, D. F., König, I. R., & Ziegler, A.
- Affiliations
- University Hospital Schleswig-Holstein (DE)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-05-26
- Volume
- 26
- Issue
- 14
- Pages
- 1752-1758
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2894507
- OpenAlex
- W2128207034
- Cited by
- 269
- References
- 57
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 7 · 2023: 10 · 2022: 16 · 2021: 18 · 2020: 17 · 2019: 13 · 2018: 22 · 2017: 16 · 2016: 27 · 2015: 16 · 2014: 22 · 2013: 21 · 2012: 23
- FWCI
- 12.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Minnesota
- Deutsche Forschungsgemeinschaft (2250/3)
- National Institutes of Health
- National Institute on Aging (R01AG021917)
- National Heart, Lung, and Blood Institute (R01HL049609)
- National Institute of General Medical Sciences (R01GM031575)
- National Institute of Diabetes and Digestive and Kidney Diseases
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- random forest, genome-wide association study, high-dimensional data, permutation importance, single nucleotide polymorphisms, gene-gene interaction, gene-environment interaction, Crohn's disease, type 2 diabetes, computational efficiency, backward elimination, disease prediction
- MeSH terms
- Crohn Disease, Humans, Software, Genome, Human, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study
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