The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Random Forests for Genetic Association Studies
Open accessAbstract
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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Cited by
Hyperparameters and tuning strategies for random forest
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Metadata
- Authors
- Goldstein, B. A., Polley, E. C., & Briggs, F. B. S.
- Venue
- Statistical Applications in Genetics and Molecular Biology
- Publisher
- Walter de Gruyter GmbH
- Published
- 2011-07-12
- Volume
- 10
- Issue
- 1
- Pages
- 32-32
- ISSN
- 1544-6115 (ISSN-L)
- 2194-6302
- OpenAlex
- W2044863747
- Cited by
- 272
- References
- 58
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- green
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