Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
This work addresses schizophrenia diagnosis for medical researchers, but it is incremental as it applies existing methods to new data types without major methodological innovation.
The study tackled classifying schizophrenia patients from healthy controls using SNPs, DNA methylation, and fMRI data, finding that Kernel and Multiple Kernel CCA methods achieved significantly higher accuracies than regularized linear CCA, with maximal accuracy when combining DNA methylation and fMRI data.
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 Kernel CCA represent new avenues for studying schizophrenia, because, to our knowledge, these methods have not been used on these data before. Classification is performed via k-means clustering on the kernel matrix outputs of the Kernel and Multiple Kernel CCA algorithm. Accuracies of the Kernel and Multiple Kernel CCA classification are compared to that of the regularized linear CCA algorithm classification, and are found to be significantly more accurate. Both algorithms demonstrate maximal accuracies when the combination of DNA methylation and fMRI data are used, and experience lower accuracies when the SNP data are incorporated.