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Random Jungle specifications


Unique identifier OMICS_10093
Name Random Jungle
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Windows
Computer skills Advanced
Stability No
Maintained No


No version available


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Publication for Random Jungle

Random Jungle citations


Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application

Plant Biotechnol J
PMCID: 5258866
PMID: 27696619
DOI: 10.1111/pbi.12645

[…] ols to assess quality. PLINK employs standard regression for GWAS. However, standard regression may not be sensitive enough when the frequency of the variant is low (Ma et al., ). Other tools such as Random Jungle (Schwarz et al., ) use fast random forest methods, which can be more sensitive than traditional statistical approaches. Further popular tools for GWAS also include TASSEL (Bradbury et al […]


Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US

PMCID: 5243845
PMID: 28154817
DOI: 10.3389/fvets.2017.00002
call_split See protocol

[…] v × nlog(n)), where v is the number of variables and n is the number of observations. This analysis took around 45 min to complete on a standard MacBook Pro®, though other packages such as ranger and random jungle may achieve faster performances for larger data sets and down-sampling can further optimize run times ().Finally, using the observed and predicted animal movements, we conducted an SNA t […]


Building a genetic risk model for bipolar disorder from genome wide association data with random forest algorithm

Sci Rep
PMCID: 5206749
PMID: 28045094
DOI: 10.1038/srep39943

[…] idate SNPs in the GAIN dataset). The results from the growing of ensemble trees as a forest could provide a list of important variables for disease outcome. The RF procedures were performed using the Random Jungle package, which facilitates the rapid analysis of large-scale GWA data. Detailed procedures are described in the following steps:Two-thirds of the subjects in the GWA dataset were taken a […]


Efficient Strategy to Identify Gene Gene Interactions and Its Application to Type 2 Diabetes

PMCID: 5287119
PMID: 28154506
DOI: 10.5808/GI.2016.14.4.160

[…] LD) in case and control groups, have been recently proposed []. However, analyzing a large number of SNPs in a GWAS is computationally very intensive, and various approaches, such as MDR [], BEAM [], Random Jungle [], PLINK [], and BOolean Operation-based Screening and Testing (BOOST), have been proposed to enable gene-gene interactions on a genome-wide scale.In this paper, we considered BOOST met […]


Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data

BMC Proc
PMCID: 5133476
PMID: 27980627
DOI: 10.1186/s12919-016-0021-1
call_split See protocol

[…] ross runs.For this analysis, we ran the above selection algorithm using RF on one of the simulated data sets for the systolic blood pressure (SBP) and Q1 (permuted) traits. We used the parallelizable Random Jungle (RJ) software to allow for the large number of input variables []. We ran RJ with regression trees (numeric inputs and outputs), 60,000 variables sampled at each node (mtry), and 4000 tr […]


The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine

PLoS One
PMCID: 4981436
PMID: 27513748
DOI: 10.1371/journal.pone.0158016
call_split See protocol

[…] ction effects, EC uses ReliefF to calculate the interaction component for each SNP, providing a more robust indication of a variant’s utility in a posterior epistatic network. The other half of EC is Random Jungle, an implementation of Random Forest, which is used to calculate the main effect contribution of the SNP importance scores. By filtering variants before the construction of an epistasis n […]


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Random Jungle institution(s)
Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
Random Jungle funding source(s)
DFG (KO 2250/3-1); intramural funding from Medical Faculty of the University at Lübeck (E32-2009, SPP2); NIH grants 5RO1-HL049609-14, 1R01-AG021917-01A1; University of Minnesota; Minnesota Supercomputing Institute; GAW grant, R01-GM031575; ENGAGE (grant agreement number 201413); Atherogenomics (grant agreement number 01GS0831)

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