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Recently a network-constraint regression model[1] is proposed to incorporate the prior biological knowledge to perform regression and variable selection. In their method, a l1-norm of the coefficients is defined to impose sparse, meanwhile a Laplacian operation on the biological graph is designed to encourage smoothness of the coefficients along the network. However the grouping effect of their Laplacian...
We report multifactorial analysis of candidate mechanisms of Alzheimer's disease utilizing high content analysis, gene expression microarray, and linear regression model to integrate neuronal imaging data with hippocampal gene expression data. Our analysis led to the identification of several genes that may contribute to different image traits or phenotypes in the amyloid-beta (Abeta) injured neurons...
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