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Abstrakt
Discovering Pharmacogenetic Latent Structure Features Using Divergences
Clive E Bowman
Non-linear divergence functions, as sufficient additive contrast-based measures of direct evidence, offer a smooth universal information basis to deconstruct the stochastic question actually being asked in pharmacogenetic experiments. The orthogonal decomposition of individualized marginal divergences is introduced using entropy and commonalities with PLS-DA. Feature selection is shown in examples of the genetic discriminant analysis of:-up to 3-class diseases; gene by drug treatment studies; and drug-induced multiple adverse events. Analysis over multiple data types, aggregates and dummy indicators is presented. Interaction and epistasis analysis is exemplied. Signal stability, smoothing, approximations and permutation based significance tests are discussed.