Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm.

ABSTRACT:

Motivation

Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the

SUBMITTER: Yan J 

PROVIDER: S-EPMC4147918 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature