The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Random forests for genomic data analysis
Open accessAbstract
Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly data adaptive, applies to “large p , small n ” problems, and is able to account for correlation as well as interactions among features. This makes RF particularly appealing for high-dimensional genomic data analysis. In this article, we systematically review the applications and recent progresses of RF for genomic data, including prediction and classification, variable selection, pathway analysis, genetic association and epistasis detection, and unsupervised learning. ► Applications and recent progresses of random forests for genomic data analysis. ► Methods for variable selection by random forests and random survival forests. ► Classification and prediction of random forests using high-dimensional genomic data. ► Genetic association and epistasis detection using random forests on GWA data.
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MissForest—Non-parametric missing value imputation for mixed-type data
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Self-citationVariable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
Detecting gene–gene interactions that underlie human diseases
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Random Forests and Adaptive Nearest Neighbors
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Metadata
- Authors
- Chen, X., & Ishwaran, H.
- Venue
- Genomics
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 99
- Issue
- 6
- Pages
- 323-329
- ISSN
- 0888-7543 (ISSN-L)
- OpenAlex
- W2091374137
- Cited by
- 953
- References
- 8
- FWCI
- 16.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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