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Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizophrenia and 104 healthy controls. Kernel and Multiple...
In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new...
We investigate the correspondence between genetic variations with single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI). A group sparse canonical correlation analysis method (group sparse CCA) was proposed to explore the correlation between these two types of data, which are high dimensional with small number of samples. It can exploit the...
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