Random forests on distance matrices for imaging genetics studies
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Summary
A non-parametric regression methodology for detecting genetic variants associated to quantitative phenotypes, obtained using neuroimaging techniques, representing the human brain’s structure or function is proposed, and ways to learn distances directly from the data using manifold learning techniques are discussed.
- Type
- preprint
- Published
- 2013-09-24
- Cited by
- 7
- References
- 65
- Access
- Open access
- OpenAlex
- https://openalex.org/W2083544905
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:34944152
Keywords
Imaging genetics, Random forest, Distance matrices in phylogeny, Neuroimaging, Artificial intelligence
References
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- Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease
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Cited by
- Block-based selection random forest for texture classification using multi-fractal spectrum feature
- A review of multivariate analyses in imaging genetics
- Random forest regression for manifold-valued responses
- A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space
- A review on longitudinal data analysis with random forest
- Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer’s disease
- Intelligent Monitoring System of Cremation Equipment Based on Internet of Things
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