P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data
Sprache des Titels:
The joint analysis of genetic and brain imaging
data is the key to understand the genetic underpinnings of
brain dysfunctions in several psychiatric diseases known to have
a strong genetic component. The goal is to identify associations
between genetic and functional or morphometric brain measurements.
We here suggest a machine learning method to solve
this task, which is based on the recently proposed Potential
Support Vector Machine (P-SVM) for variable selection, a
subsequent k-NN classification and an estimation of the effect
of ’correlations by chance’. We apply it to the detection of
associations between candidate single nucleotide polymorphisms
(SNPs) and volumetric MRI measurements in alcohol dependent
patients and healthy controls.