Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.
Maria VounouThomas E. NicholsGiovanni MontanaPublished in: NeuroImage (2010)
Keyphrases
- high dimensional
- genome wide association studies
- microarray data
- genetic variation
- sparse data
- complex diseases
- generalized linear models
- biological knowledge
- regression model
- high dimensionality
- multi variate
- gene expression data
- low dimensional
- sparse coding
- sequence data
- linear regression
- additive models
- feature space
- dimensionality reduction
- high dimension
- sparse regression
- sparse bayesian learning
- functional magnetic resonance imaging
- genome wide
- structured sparse learning
- high dimensional data
- variable selection
- data points
- nearest neighbor
- model selection
- human brain
- similarity search
- dimension reduction
- regression analysis
- genetic algorithm
- gene expression
- high throughput
- input space
- logistic regression
- single nucleotide polymorphisms
- relevance vector machine
- reproducing kernel hilbert space
- sparse kernel
- regression problems
- support vector regression
- association rules
- caenorhabditis elegans
- biological systems
- computational approaches
- canonical correlation analysis
- partial least squares
- dna sequences
- gene regulatory networks