Fig 1.
An illustrative figure of the architecture of the proposed transfer-learning-based deep network.
The blue box: DNN models obtained from feature screening and the corresponding parameters are fixed. The green box: the background node () capturing the infinitesimal effects and the newly added hidden layers designed to model the joint effects from selected genes. The parameters associated with the background node and the newly added hidden layers are estimated.
Fig 2.
An illustrative figure of the architecture of the proposed transfer-learning-based deep network, where no interaction between genes is assumed.
The blue box: DNN models obtained from feature screening and the corresponding parameters are fixed. The green box: the newly added hidden layers, a background node, and their associated parameters that need to be estimated.
Table 1.
The comparisons of type I errors based on 5000 Monte Carlo simulations.
Fig 3.
The comparisons of power under 5% significance level based on 5000 Monte Carlo simulations.
Linear (90%): 90% of genetic variants on the causal gene is predictive. Linear (10%): 10% of genetic variants on the causal gene is predictive. Interaction: pairwise interaction effects. Non-linear (cos): genetic variants on the causal gene affect the outcome through a cosine function.
Fig 4.
The comparisons of prediction accuracy for continuous outcomes.
Genes with p-values less than 0.001 are considered significant.
Fig 5.
The comparisons of prediction accuracy for binary outcomes.
Genes with p-values less than 0.001 are considered significant.
Fig 6.
The Manhattan plot for AV45 and FDG using the DNN-screen method.
Fig 7.
The Pearson correlations between the predicted and observed values for AV45 and FDG.
Genes are pre-selected under the p-value threshold of 0.001 for DNN-transfer, SKAT-linear, SKAT-optimal and ACAT.