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.
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Related Subject Headings
- Polymorphism, Single Nucleotide
- Humans
- Genome-Wide Association Study
- Data Interpretation, Statistical
- Bioinformatics
- Artificial Intelligence
- Algorithms
- 49 Mathematical sciences
- 31 Biological sciences
- 06 Biological Sciences
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Polymorphism, Single Nucleotide
- Humans
- Genome-Wide Association Study
- Data Interpretation, Statistical
- Bioinformatics
- Artificial Intelligence
- Algorithms
- 49 Mathematical sciences
- 31 Biological sciences
- 06 Biological Sciences