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Figure 4 | Algorithms for Molecular Biology

Figure 4

From: Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data

Figure 4

Penalized nonlinear canonical correlation analysis for longitudinal data. Each of the p longitudinal measured risk factors is summarized into a slope (S) and an intercept (I) variable. The SNP variables are transformed via optimal scaling within each step of the algorithm and hereafter penalized; SNPs that contribute little, based upon their weights (v) are eliminated (dotted lines) and the relevant variables remain. The obtained canonical variates ω and ξ correlate maximally.

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