Random forests for genetic association studies.
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.
Quantitative Sciences Unit, Department of Medicine, Stanford University, USA.
Benjamin A
BA
Goldstein
Eric C
EC
Polley
Farren B S
FB
Briggs
D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D020641:Polymorphism, Single Nucleotide