A random forest approach to the detection of epistatic interactions in case-control studies

Jiang, Rui; Tang, Wenjun; Wu, Xuebing; Fu, Wenhui

doi:10.1186/1471-2105-10-s1-s65

Cited by 263 publications

(220 citation statements)

References 36 publications

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“…Interacting predictors that both have no A simple graphical method which might help to identify predictors involved in interactions consists in plotting the RF VIMs (which may also capture interaction effects) against a standard univariate statistic, see e.g. [40,50]. In the first step, a subset of potentially interesting predictors is extracted using RF.…”

Section: Predictors Involved In Interactionsmentioning

confidence: 99%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

614

455

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The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.

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Section: Predictors Involved In Interactionsmentioning

confidence: 99%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

614

455

View full text Add to dashboard Cite

show abstract

“…Bureau et al [2005] created a modification of pVI to look at the joint effects for pairs of SNPs. Jiang et al [2009] proposed a sliding window VI measure for examining interactions.…”

Section: Other Variable Importance Measuresmentioning

confidence: 99%

“…While there is potentially room for the development of VI measures that explicitly examine interactions, little work has been performed to this point. In one of the few known methods, Jiang et al [2009] proposed a sliding window method to scan for up to three-way interactions.…”

Section: Interaction Detectionmentioning

confidence: 99%

Random Forests for Genetic Association Studies

Goldstein

Polley²,

Briggs

2011

Statistical Applications in Genetics and Molecular Biology

202

172

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The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

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“…However, direct application of RF to GWAS still poses a real challenge, and only a few studies were reported in the literature. 14,17,[19][20][21] The difficulty lies in the poor quality of estimates of variables' importance when huge forests are constructed indiscriminately from the whole data, where the majority of the variables are noise. This obstacle must be overcome for GWAS applications of RF to be practically useful.…”

Section: Introductionmentioning

confidence: 99%

Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies

Yang

2013

Eur J Hum Genet

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Genome-wide association studies (GWAS) has brought methodological challenges in handling massive high-dimensional data and also real opportunities for studying the joint effect of many risk factors acting in concert as an organic group. The random forest (RF) methodology is recognized by many for its potential in examining interaction effects in large data sets. However, RF is not designed to directly handle GWAS data, which typically have hundreds of thousands of single-nucleotide polymorphisms as predictor variables. We propose and evaluate a novel extension of RF, called random forest fishing (RFF), for GWAS analysis. RFF repeatedly updates a relatively small set of predictors obtained by RF tests to find globally important groups predictive of the disease phenotype, using a novel search algorithm based on genetic programming and simulated annealing. A key improvement of RFF results from the use of guidance incorporating empirical test results of genome-wide pairwise interactions. Evaluated using simulated and real GWAS data sets, RFF is shown to be effective in identifying important predictors, particularly when both marginal effects and interactions exist, and is applicable to very large GWAS data sets.

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A random forest approach to the detection of epistatic interactions in case-control studies

Cited by 263 publications

References 36 publications

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Random Forests for Genetic Association Studies

Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies

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